System and method for reconstructing an object using a migration image

Abstract

To provide a system and a method of reconstructing an object using a transitional image.SOLUTION: A method includes a step of receiving, by an object reconstruction module, a first image and a second image. The first image includes a first region of an object and the second image comprises a second region of the object. The method also includes a step of identifying, by the object reconstruction module, a transitional image. The transitional image includes the first region of the object and the second region of the object. The method further includes a step of determining, by the object reconstruction module, that the first region of the object in the transitional image and the first region of the object in the first image are equivalent regions and a step of generating, by the object reconstruction module, a reconstruction of the object using the first image and the transitional image. The reconstruction of the object includes the first region of the object and the second region of the object and excludes equivalent regions.SELECTED DRAWING: Figure 6

Description

Technical Field

[0001] The present disclosure generally relates to reconstructing an object, and more particularly to a system and method for reconstructing an object using a transitional image.

Background Art

[0002] An object detection model processes a large amount of image data and analogizes each image independently. There is an inherent trade-off between the speed and accuracy of a deep learning model. This trade-off is amplified when training the model to detect objects whose size changes dramatically and / or parts of objects where the ratio of the overall pixel data is very small. An object detection model can be trained to accurately identify objects in multiple images, but in certain systems, the latency penalty associated with accurately detecting very small parts of an object cannot be tolerated.

Summary of the Invention

Means for Solving the Problems

[0003] According to one embodiment, the method includes steps of receiving a first image and a second image by an object reconstruction module. The first image includes a first region of an object, and the second image includes an object includes a second region of the. This method also includes identifying, by an object reconstruction module, a transition image . The transition image includes a first region of the object and a second region of the object. The method also includes determining, by the object reconstruction module, that the first region of the object in the transition image and the first region of the object in the first image are a first equivalent region (firs t equivalent region), and generating, by the object reconstruction module, a reconstruction (r econstruction) of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region.

[0004] In certain embodiments, the first image further includes a third region of the object, a fourth region of the object , and a fifth region of the object, and the transition image includes the third region of the object and also includes the fourth region of the object. In some embodiments, the method includes determining, by the object reconstruction module, the third region of the object in the transition image and the third region of the object in the first image. The images are a third equivalent region, and the object reconstruction module determines that the fourth region of the object in the transition image and the fourth region of the object in the first image are a fourth equivalent region. In certain embodiments, the reconstruction of the object excludes the third and fourth regions. In some embodiments, the method includes generating, by the object reconstruction module, for reconstructing the object, the first region of the object, the second region of the object, the third region of the object, the object's fourth region, the first region of the object, the second region of the object, the third region of the object, the object's ​​​including the step of connecting a fourth region and a fifth region of the object, where the object reconfiguration shows the entire object.

[0005] In certain embodiments, the method includes identifying, by an object reconfiguration module, a first portion of an object in a first image and configuring, by the object reconfiguration module, a first bounding box around the first portion of the object in the first image. The first bounding box may include a first region of the object. In some embodiments, the method includes identifying, by the object reconfiguration module, a second portion of the object in a transition image and configuring, by the object reconfiguration module, a second bounding box around the second portion of the object in the transition image. The second bounding box may include the first region of the object and a second region of the object. In certain embodiments, the method includes using IoU (Intersection over Union) to determine that the first region of the object in the transition image and the first region of the object in the first image are equivalent regions. In some embodiments, the transition image includes half of the first image and half of the second image. According to other embodiments, the apparatus includes one or more processors and one or more computer-readable non-transitory storage media coupled to the one or more processors.

[0006] ble non-transitory storage media) and includes instructions stored on the one or more computer-readable non-transitory storage media that, when executed by the one or more processors, cause the apparatus to perform operations including those described above. The one or more computer-readable non-transitory storage media may store instructions that, when executed by the one or more processors, cause the apparatus to perform operations including those described above. The above computer-readable non-transitory storage medium includes instructions for causing a device to perform processing including receiving a first image and a second image, the processing being executed by one or more processors. The first image includes a first region of an object, and the second image includes a second region of the object. The processing also includes processing for identifying a transition image, the transition image including the first region of the object and the second region of the object. The processing further includes processing for determining that the first region of the object in the transition image and the first region of the object in the first image are first equivalent regions, and processing for generating a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. and instructions for causing a device to perform processing including receiving a first image and a second image, the first image including a first region of an object, and the second image including a second region of the object. The processing also includes processing for identifying a transition image, the transition image including the first region of the object and the second region of the object. The processing further includes processing for determining that the first region of the object in the transition image and the first region of the object in the first image are first equivalent regions, and processing for generating a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. includes a first region of an object, and the second image includes a second region of the object. The processing also includes processing for identifying a transition image, the transition image including the first region of the object and the second region of the object. The processing further includes processing for determining that the first region of the object in the transition image and the first region of the object in the first image are first equivalent regions, and processing for generating a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. also includes processing for identifying a transition image, the transition image including the first region of the object and the second region of the object. The processing further includes processing for determining that the first region of the object in the transition image and the first region of the object in the first image are first equivalent regions, and processing for generating a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. including the first region of the object and the second region of the object. The processing further includes processing for determining that the first region of the object in the transition image and the first region of the object in the first image are first equivalent regions, and processing for generating a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. processing for determining that the first region of the object in the transition image and the first region of the object in the first image are first equivalent regions, and processing for generating a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. and processing for generating a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region.

[0007] According to another embodiment, one or more computer-readable storage media embody instructions for causing a processor to perform processing including receiving, by an object reconstruction module, a first image and a second image. The first image includes a first region of an object, and the second image includes a second region of the object. The processing also includes processing for identifying, by the object reconstruction module, a transition image, the transition image including the first region of the object and the second region of the object. The processing further includes processing for determining, by the object reconstruction module, that the first region of the object in the transition image and the first region of the object in the first image are first equivalent regions, and processing for generating, by the object reconstruction module, a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. when executed by a processor, to cause the processor to perform processing including receiving, by an object reconstruction module, a first image and a second image. The first image includes a first region of an object, and the second image includes a second region of the object. The processing also includes processing for identifying, by the object reconstruction module, a transition image, the transition image including the first region of the object and the second region of the object. The processing further includes processing for determining, by the object reconstruction module, that the first region of the object in the transition image and the first region of the object in the first image are first equivalent regions, and processing for generating, by the object reconstruction module, a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. receiving, by an object reconstruction module, a first image and a second image. The first image includes a first region of an object, and the second image includes a second region of the object. The processing also includes processing for identifying, by the object reconstruction module, a transition image, the transition image including the first region of the object and the second region of the object. The processing further includes processing for determining, by the object reconstruction module, that the first region of the object in the transition image and the first region of the object in the first image are first equivalent regions, and processing for generating, by the object reconstruction module, a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. includes a first region of an object, and the second image includes a second region of the object. The processing also includes processing for identifying, by the object reconstruction module, a transition image, the transition image including the first region of the object and the second region of the object. The processing further includes processing for determining, by the object reconstruction module, that the first region of the object in the transition image and the first region of the object in the first image are first equivalent regions, and processing for generating, by the object reconstruction module, a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. The processing also includes processing for identifying, by the object reconstruction module, a transition image, the transition image including the first region of the object and the second region of the object. The processing further includes processing for determining, by the object reconstruction module, that the first region of the object in the transition image and the first region of the object in the first image are first equivalent regions, and processing for generating, by the object reconstruction module, a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. including the first region of the object and the second region of the object. The processing further includes processing for determining, by the object reconstruction module, that the first region of the object in the transition image and the first region of the object in the first image are first equivalent regions, and processing for generating, by the object reconstruction module, a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. The processing further includes processing for determining, by the object reconstruction module, that the first region of the object in the transition image and the first region of the object in the first image are first equivalent regions, and processing for generating, by the object reconstruction module, a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. processing for determining, by the object reconstruction module, that the first region of the object in the transition image and the first region of the object in the first image are first equivalent regions, and processing for generating, by the object reconstruction module, a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. and processing for generating, by the object reconstruction module, a reconstruction of the object using the first image and the transition image. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. The reconstruction of the object includes the first region of the object and the second region of the object, excluding the first equivalent region. excluding the first equivalent region.

[0008] The technical advantages of certain embodiments of the present disclosure may include one or more of the following. The present disclosure describes systems and methods for reconstructing objects using transition images. In particular embodiments, a bounding box reduction algorithm for reconstructing an object may be used in combination with a standard non-maximum suppression algorithm that operates on a per-image basis to reduce the bounding boxes included within a single image and a single analogy result (non-maximum um suppression algorithms). In certain embodiments, a transition image is generated by overlaying two images at a predetermined percentage. In certain embodiments of the present disclosure, a predetermined percentage of 50% is used, but the present disclosure contemplates any suitable predetermined percentage (e.g., 25 percent, 75 percent, etc.). In certain embodiments, the systems and methods disclosed herein may use reduction criteria such as an IoU score between two or more boxes, equivalence of detection labels, and / or a threshold of a confidence score.

[0009] In certain embodiments of the present disclosure, the systems and methods disclosed herein may be used across several object classes within the same image. For example, in some embodiments it is possible to logically reduce bounding boxes belonging to the same class, and as a result some fully reconstructed objects may be generated. In certain embodiments an IoU is used to calculate an equivalent area of a bounding box and / or other region of interest, in a similar way to how a standard non-maximum suppression algorithm is used to calculate an equivalence. This may be the case. The systems and methods described in this disclosure can be generalized to various transportation infrastructures, including railways, roads, and waterways. It can be generalized to various transportation infrastructures, including railways, roads, and waterways.

[0010] Other technical advantages will be readily apparent to those skilled in the art from the following drawings, description, and claims. Further, while specific advantages are listed above, various embodiments may include all or some of the listed advantages, or none at all. To assist in understanding the present disclosure, reference is made to the following description in conjunction with the accompanying drawings.

Brief Description of the Drawings

[0011]

[0012]

Figure 1

Figure 2

Figure 3

Figure 4

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Figure 6

Figure 6

Figure 7

Modes for Carrying Out the Invention

[0013] FIGS. 1 to 7 show an exemplary system and method for reconstructing an object using a transition image. FIG. 1 is an example of a system for reconstructing an object using a transition image. FIG. 1 is an example of a system for reconstructing an object using a transition image. shows the system shown, and FIG. 2 shows an exemplary image that can be used by the system of FIG. 1 is shown. FIG. 3 shows an exemplary transition image that can be used by the system of FIG. 1, and FIG. 4 shows an exemplary equivalent region that can be determined by the system of FIG. 1. FIG. 5 shows an example of object reconstruction that can be generated by the system of FIG. 1 is shown. FIG. 6 shows an exemplary method of reconstructing an object using a transition image. FIG. 7 shows an exemplary computer system that can be used by the systems and methods described herein is shown. FIG. 7 shows an exemplary computer system that can be used by the systems and methods described herein is shown.

[0014] FIG. 1 shows an exemplary system 100 for reconstructing an object using a transition image is shown. System 100 or a part thereof can be associated with an entity, and can include any organization such as a company, corporation (e.g., a railway company, a transportation company, etc.) or a government agency (e.g., a transportation bureau, a public security bureau, etc.) that reconstructs an object using a transition image The elements of system 100 can be implemented using any suitable combination of hardware, firmware, and software For example, the elements of system 100 may be implemented using one or more components of the computer system of FIG. 7. System 100 includes a network 110, a railway environment 120, a railway line 130, a railway vehicle 140, an image capture module 150, an object 16 0, an object reconstruction module 170, an image 172, a transition image 174, a bounding box 176, an equivalent region, and an object reconstruction 180.

[0015] ​​​Network 110 can be any type of network that facilitates communication between the components of system 100. One or more portions of network 110 can be an ad hoc network, intranet, extranet, virtual private network (VPN), local area network (LAN), wireless LAN (WLAN), wide area network (WAN), wireless W AN (WWAN), metropolitan area network (MAN), a portion of the Internet, a portion of the public switched telephone network (PSTN), cellular telephone network, 3G network, 4G network, 5G network, LTE (Long Term Evolution) cellular communication network, a combination of two or more of these, or other suitable types of networks. One or more portions of network 110 can include one or more access (e.g., mobile access), core, and / or edge networks. Network 110 can be a private network, public network, connection via the Internet, mobile network, WI-FI network, Bluetooth (registered trademark) network, etc. One or more components of system 100 can communicate via network 110. For example, object reconstruction module 170 can communicate via network 110, including receiving information from image capture module 150.

[0016] The railway environment 120 of system 100 is an area that includes one or more railway lines 130. The railway environment 120 can be related to a division and / or subdivision. A division is a portion of the railway under the supervision of a supervisor. A subdivision is a sub-portion of a division. ​ is also a very small part. The sub - compartments can be a crew district and / or can be a branch line. In the embodiment shown in FIG. 1, the railway environment 12 0 includes a railway track 130, a railway vehicle 140, an image capture module 150, and an object 160.

[0017] The railway track 130 of the system 100 is a structure that enables the railway vehicle 140 to move by providing a surface for the wheels of the railway vehicle 140 to roll on. In a particular embodiment , the railway track 130 includes rails, fasteners, sleepers, ballast, etc. The railway vehicle 140 of the system 10 0 is a vehicle that can move along the railway track 130. The railway vehicle 140 can be a geometry vehicle, a locomotive, a passenger car, a freight car, a covered car, a long - goods car, a tank car, etc. In a particular embodiment, the railway vehicle 140 may be associated with one or more image capture modules 15 0.

[0018] The image capture module 150 of the system 100 is a component that captures an image 17 2 of the object 160. The object 160 of the system 100 is a transportation infrastructure component such as a road, a railway, an airway, a waterway, a canal, a pipeline, and a terminal component. The object 160 can include components within the railway environment 120 such as the railway track 130, debris (e.g., , rubble, wreckage, ruins, litter, trash, brush, etc.), pedestrians (e.g., trespassers ), animals, vegetation, ballast, etc. The object 160 can be a joy (trespasser)), animals, vegetation, ballast, etc. The object 160 can be a joy joint, switch, frog, rail head (rail head), anchor, fastener, gage plate, ballast, sleeper (e.g., sleeper made of concrete and wooden sleeper) and other components of the railway track 130 may be included. The image 172 of the system 100 shows the physical form of one or more objects 160. The image 172 may include a digital image, a photograph, etc. The image 172 includes image 172a, image 1 72b, etc., and continues up to image 172n, where 172n represents any suitable number. .

[0019] The image capture module 150 may include one or more cameras, lenses, sensors, optical devices, lighting elements, etc. For example, the image capture module 150 may include one or more cameras (e.g., high-resolution cameras, line scan cameras, stereo cameras, etc.) that automatically capture the image 172 of the object 1 60. In certain embodiments, the image capture module 150 is attached to the railway vehicle 140. The image capture mo dule 150 may be attached to the railway vehicle 140 at any suitable location that provides a clear view of the railway environment 120. For example, the image capture module 150 may be attached to the front end (e.g., front bumper) of the railway vehicle 140 to provide a downward view of the railway track 130. Another example, the image capture module 150 may be attached to the front end (e.g., front glass) of the railway vehicle 140 to provide a front view of the railway track 130. As yet another example, the image capture module 150 may be attached to the front end (e.g., front bumper) of the railway vehicle 140 to provide a downward view of the railway track 130. 130. Another example, the image capture module 150 may be attached to the front end (e.g., front glass) of the railway vehicle 140 to provide a front view of the railway track 130. As yet another example, the image capture module 150 may To provide a rear view of the railway line 130, it may be attached to the rear end of the railway vehicle 140 (e.g., the rear windshield).

[0020] The image capture module 150 can automatically capture a still image or a video 172 while the railway vehicle 140 moves along the railway line 130. The image capture module 150 can automatically capture any suitable number of still images or videos 172. For example, the image capture module 150 can use an encoder (e.g., a position encoder) to automatically capture an image 172 as a function of distance in order to trigger a camera (e.g., a line scan camera). As another example, the image capture module 150 can automatically capture a predetermined number of still images 172 per second, per minute, per hour, etc. In some embodiments, the image capture module 150 can inspect an image 172 for an object 160. For example, the image capture module 150 can detect an object 160 in the image 172 using one or more models. The model can include one or more object detection models, machine learning models, machine vision models, deep learning models, etc. The image capture module 150 can transmit one or more images 172 of one or more objects 160 to the object reconstruction module 170. The object reconstruction module 170 of the system 100 is a component that regenerates an object 160 from one or more images 172 and / or one or more transition images 174. In a particular embodiment, the object reconstruction module 170 is one or more of the system 100 to

[0021] The object reconstruction module 170 of the system 100 is a component that regenerates an object 160 from one or more images 172 and / or one or more transition images 174. In certain embodiments, the object reconstruction module 170 is one or more of the system 100 Receive the image 172 from the upper component. For example, the object reconstruction module 170 may receive the images 172a and 172b from the image capture module 150 . The multiple images 172 may include one or more parts of a single object 160. For example , the image 172a may include the first part of the object 160, and the image 172b may include the second part of the object 160.

[0022] Each object 160 may be represented as a region. For example, the object 160 (e.g., , the joint plate of the rail) may have five regions such that the image 172a includes four consecutive regions of the object 160 and the image 172b includes the remaining fifth region of the object 160. As another example, the object 160 may have five regions such that the image 172a includes three consecutive regions of the object 160 and the image 172b includes the remaining fourth and fifth regions of the object 160.

[0023] In certain embodiments, the object reconstruction module 170 performs an analogy on one or more images 172 and / or the transition image 174. The object reconstruction module 17 0 may perform the analogy by executing an object detection algorithm to detect the object 160 within the image 172 and / or the transition image 174. In response to performing the analogy , the object reconstruction module 170 may generate one or more bounding boxes 176 around one or more objects 160 (or a part thereof). The bounding box 176 is a contour line that identifies the object 160. In certain embodiments Each bounding box 176 is defined by the x and y coordinates that identify the four corners of a rectangle and is a rectangular box around the perimeter of object 160. Bounding box 1 76 includes bounding box 176a, bounding box 176b, bounding ing box 176c, etc., and continues up to bounding box 176n, where 174n represents any suitable number.

[0024] In certain embodiments, the image capture module 150 constructs a bounding box 176 around the perimeter of an object 160 (or a portion thereof) within the image 172. For example, the object reconstruction module 170 may identify a first portion of the object 160 within the image 172a and construct a bounding box 176a around the perimeter of the first portion of the object 160 within the image 172a. As another example, the object reconstruction module 170 may identify a second portion of the object 160 within the image 172b and construct a bounding box 176b around the perimeter of the second portion of the object 160 within the image 172b. As yet another example, the object reconstruction module 170 may identify a third portion of the object 160 within the transition image 174a and construct a bounding box 176c around the perimeter of the third portion of the object 160 within the transition image 176a. In some embodiments, the region of the object 160 is defined by the bounding box 17 6. For example, in the case of an object having five regions, the first portion of the object 160 enclosed by the bounding box 176a within the image 172a is such that In some other embodiments, the object reconstruction module 170 may identify a third portion of the object 160 within the transition image 174a and construct a bounding box 176c around the perimeter of the third portion of the object 160 within the transition image 176a. The third portion of the object 160 within the transition image 176a is bounded by the bounding box 176c.

[0025] In some embodiments, the area of the object 160 is defined by the bounding box 17 6. For example, in the case of an object having five regions, the first portion of the object 160 enclosed by the bounding box 176a within the image 172a is such that It is also possible to define a first region, a second region, a third region, and a fourth region of the object 160 The second part of the object 160 surrounded by the bounding box 176b in the image 172b 0 may define a fifth region of the object 160, and the transition image 1 The third part of the object 160 surrounded by the bounding box 176c in 74a may define the second region, the third region, the fourth region, and the fifth region of the object 160 part may also define the second region, the third region, the fourth region, and the fifth region of the object 160 as well.

[0026] In certain embodiments, the object reconstruction module 170 generates a transition image 174 from the image 172. The transition image 174 is an image that shares the same pixels with one or more other images 172. For example, the image 172a and the transition image 174a may share a predetermined ratio of the same pixels (e.g., 25, 50, or 75%). The transition image 174 includes up to the transition image 174a, the transition image 174b, and the transition image 174n, where 176n represents any suitable number. In some embodiments, the object reconstruction module 170 generates the transition image 174 by overlapping two or more images 1 72. For example, the object reconstruction module 170 may overlap a part (e.g., 50%) of the image 172a and a part (e.g., 50%) of the image 172b to generate the transition image 174a such that the transition image 174a includes the overlapping part of the image 172a and the image 172b. In some embodiments, the image capture module 150 captures the transition image 174. For example, the image capture module 150 may capture the transition image 174 with a predetermined amount of overlap (e.g., 25, 50, or 75 percent of overlap

[0027] In some embodiments, the object reconstruction module 170 generates the transition image 174 by overlapping two or more images 1 72. For example, the object reconstruction module 170 may overlap a part (e.g., 50%) of the image 172a and a part (e.g., 50%) of the image 172b to generate the transition image 174a such that the transition image 174a includes the overlapping part of the image 172a and the image 172b. In some embodiments, the image capture module 150 captures the transition image 174. For example, the image capture module 150 may capture the transition image 174 with a predetermined amount of overlap (e.g., 25, 50, or 75 percent of overlap In some embodiments, the object reconstruction module 170 generates the transition image 174 by overlapping two or more images 1 72. For example, the object reconstruction module 170 may overlap a part (e.g., 50%) of the image 172a and a part (e.g., 50%) of the image 172b to generate the transition image 174a such that the transition image 174a includes the overlapping part of the image 172a and the image 172b. In some embodiments, the image capture module 150 captures the transition image 174. For example, the image capture module 150 may capture the transition image 174 with a predetermined amount of overlap (e.g., 25, 50, or 75 percent of overlap In some embodiments, the image capture module 150 captures the transition image 174. For example, the image capture module 150 may capture the transition image 174 with a predetermined amount of overlap (e.g., 25, 50, or 75 percent of overlap It may be configured to capture subsequent images 172 including (become).

[0028] In certain embodiments, each transition image 174 includes one or more regions of the object 160 For example, in the case of an object having five regions, the image 172a may include the first region, the second region, the third region, and the fourth region, and the image 172b may include the fifth region, and the transition image 174a may include the second, third, and fourth regions of the image 172a and the fifth region of the image 172b.

[0029] In certain embodiments, the object reconstruction module 170 determines one or more equivalent regions 1 78. The equivalent region 178 is a region within the images 172 and the transition image 1 74 that share the same pixels. The equivalent region 178 includes equivalent regions 178a, equivalent regions 178b, etc. up to equivalent regions 178n, where 178n represents any suitable number. The object reconstruction module 170 may determine the equivalent region 178 between one or more images 172 and one or more transition images 174. For example, the object reconstruction module 170 may determine that both the image 17 2a and the transition image 174a include the second, third, and fourth regions of the object 160 In certain embodiments, the object reconstruction module 170 excludes the equivalent region 178 from the object reconstruction 180. In some embodiments, the object reconstruction module 170 uses the IoU, the equivalence of the detection labels, and / or a threshold of the confidence score to determine that the first region of the object 160 in the transition image 174 and the first region of the object 160 in the image 172a are the equivalent region 178

[0030] ​In certain embodiments, the object reconstruction module 170 generates an object reconstruction 1 80. Each object reconstruction 180 is a global representation of the object 160 . For example, the object reconstruction 180 may include the first, second, third, and fourth regions of the image 172a and the fifth region of the image 172b. The object reconstruction 180 includes object reconstructions 180a, object reconstructions 180b, etc., and continues up to object reconstruction 180n, where 180n represents any suitable number. In certain embodiments, the object reconstruction module 170 generates the object reconstruction 180 using one or more images 172 and one or more transition images 174. For example, the object reconstruction module 170 may generate the object reconstruction 180a by concatenating the first, second, third, and fourth regions from the image 172a and the fifth region from the transition image 174a. In certain embodiments, the object reconstruction module 170 excludes the equivalent regions 178 to avoid overlapping of the regions of the object 160 when generating the object reconstruction 180. For example, if both the image 172a and the transition image 174a include the second, third, and fourth regions of the object 160, the object reconstruction module 170 may exclude the second, third, and fourth regions of the transition image 174a to avoid overlapping of these regions in the object reconstruction 180a. During operation, the image capture module 150 of the system 100 is attached to the railway vehicle 140.

[0031]

[0032] ​​The image capture module captures images 172a and 172b of the object 160 while the railway vehicle 140 moves along the railway track 130 of the railway environment 120. The object reconstruction module 170 of the system 100 receives the images 172a and 172b of the object 160 (e.g., rail joint bar) from the image capture module 150 via the network 110. The object reconstruction module 170 uses one or more object detection models to detect the first part of the object 160 in the image 172a. The object reconstruction module 170 constructs a bounding box 176a around the periphery of the first part of the object 160 in the image 172a. The object 160 is represented as five regions, and the first part of the image 172a surrounded by the bounding box 176a includes the first region, the second region, the third region, and the fourth region of the object 160. The image 172b includes the second part and the fifth region of the object 160. However, the object reconstruction module 170 may not detect the second part of the object 160 due to the size of the second part relative to the overall size of the image 172b. The object reconstruction module 170 generates a transition image 174a by overlapping the image 172a and the image 172b such that the transition image 174a includes half of the image 172a and half of the image 172b. The object reconstruction module 170 detects the third part of the object 160 in the transition image 174a and constructs a bounding box 176c around the periphery of the third part of the object 160. The bounding box 176c

[0033] ​​​​​​​​​​​​​​​​​ includes the second, third, fourth, and fifth regions of the object 160. The object reconfiguration module 170 determines that the second, third, and fourth regions of the image 172a and the transition image 174a are equivalent regions 178. The object reconfiguration module 170 excludes the equivalent regions 178 (e.g., the third, fourth, and fifth regions of the transition image 174a) in the object reconfiguration 180 to avoid overlapping of the regions of the object 160. The object reconfiguration module 170 generates the object reconfiguration 180 of the object 160 by connecting the first, second, third, and fourth regions of the image 172a and the fifth region of the transition image 174a. In this way, the system 100 can be used to accurately regenerate the entire object 160 even when a small part of the object 160 is not detected in the image 172b.

[0034] FIG. 1 shows a specific arrangement of the network 110, the railway environment 120, the railway line 130, the railway vehicle 140, the image capture module 150, the object 160, the object reconfiguration module 1 70, the image 172, the bounding box 176, the transition image 174, the equivalent region 178, and the object reconfiguration 180. This specification contemplates any suitable arrangement of the network 110, the railway environment 120, the railway line 130, the railway vehicle 140, the image capture module 150, the object 160, the object reconfiguration module 170, the image 172, the bounding ing box 176, the transition image 174, the equivalent region 178, and the object reconfiguration 180.

[0035] ​​​​​​​​For example, the image capture module 150 and the object reconstruction module 170 can be combined into a single module.

[0036] FIG. 1 shows a network 110, a railway environment 120, a railway track 130, a railway vehicle 140, an image capture module 150, an object 160, an object reconstruction module 1 70, an image 172, a bounding box 176, a transition image 174, an equivalent region 178, and an object reconstruction 180. This specification contemplates any suitable number of network 110, railway environment 120, railway track 130, railway vehicle 140, image capture module 150, o bject 160, object reconstruction module 170, image 172, transition image 174 , bounding box 176, equivalent region 178, and object reconstruction 180. For example, the system 100 may include a plurality of railway environments 120, a plurality of image capture modules 150 and / or a plurality of object reconstruction modules 170.

[0037] FIG. 2 shows an exemplary image 172 that can be used by the system of FIG. 1. In the embodiment shown in FIG. 2, the image 172 includes an image 172a and an image 172b. The image 172a includes a part of an object (e.g., the object 160 in FIG. 1), and the image 172b includes the remaining part of the same object. The part of the object in the image 172a is surrounded by a bounding box 176a, and the part of the object in the image 172b is surrounded by a bounding box 176b.

[0038] To help understand the entire object within the image 172, the object is divided into five​ It is divided into regions. The parts of the object in Image 172a include Region 1, Region 2, Region 3, and Region 4, and the part of the object in Image 172b includes Region 5. When the object is represented as two independent images 172, the reconstruction of the entire object is the bounding box 176a from Image 1 72a (including Regions 1 to 4), followed by the bounding box 176b from Image 172b (including Region 5). The connection of the bounding box 176a and the bounding box 176b generates a complete set from Region 1 to 5.

[0039] As shown in FIG. 2, Image 172b includes one-fifth of the object, which is a relatively small part of the object. The object reconstruction module in FIG. 1 can be trained to accurately detect the relatively small part of the object in Image 172b, but this process may significantly reduce the object detection speed. In certain embodiments, the object reconstruction module may not be able to absorb the delay penalty for detecting Region 5 in Image 172b. However, the object reconstruction module needs to capture the entire object. To solve this problem, the object reconstruction module can use the transition image as described in FIG. 3 below to accurately detect the entire object without incurring a complete delay penalty.

[0040] FIG. 3 shows a transition image 174a that can be used by the system of FIG. 1. Image 172a and Image 172b are each divided into two parts, namely the first part and the second part. Image 1 The second part of 72a includes regions 2, 3, and 4 of the bounding box 176a, and the first part of the image 172b includes region 5 of the bounding box 176b. In a specific embodiment, the object reconstruction module may generate the transition image 174a to include the second part of the image 172a and the first part of the image 172b by overlaying the second part of the image 172a on the first part of the image 172b. In some embodiments, the transition image 174a is generated by the image capture module. An independent analogy is executed for each of the image 172a, the image 172b, and the transition image 174a in FIG. 3. In response to the execution of the analogy, the bounding box 176a is configured on the image 172a, the bounding box 176b is configured on the image 172b, and the bounding box 176c is configured on the transition image 174a. The image 172a includes regions 1, 2, 3, and 4, the image 172b includes region 5, and the transition image 174a includes regions 2, 3, 4, and 5. Since the transition image 174a exists, the object reconstruction module that sacrifices accuracy for speed can accurately detect all the objects in the images 172a and 172b. Even if the object reconstruction module cannot detect region 5 of the object in the image 172b, the pixels originally included in the image 172b are also included in the transition image 174a. Therefore, the detection of the objects in the images 172a and 174a is a complete detection. The analogy for additional images (e.g., the transition image 174a) incurs a delay penalty. The second part of the 72a includes the regions 2, 3, and 4 of the bounding box 176a, and the first part of the image 172b includes the region 5 of the bounding box 176b. In a specific embodiment, the object reconstruction module may generate the transition image 174a to include the second part of the image 172a and the first part of the image 172b by overlaying the second part of the image 172a on the first part of the image 172b. In some embodiments, the transition image 174a is generated by the image capture module. An independent analogy is executed for each of the image 172a, the image 172b, and the transition image 174a in FIG. 3. In response to the execution of the analogy, the bounding box 176a is configured on the image 172a, the bounding box 176b is configured on the image 172b, and the bounding box 176c is configured on the transition image 174a. The image 172a includes regions 1, 2, 3, and 4, the image 172b includes region 5, and the transition image 174a includes regions 2, 3, 4, and 5. Since the transition image 174a exists, the object reconstruction module that sacrifices accuracy for speed can accurately detect all the objects in the images 172a and 172b. Even if the object reconstruction module cannot detect region 5 of the object in the image 172b, the pixels originally included in the image 172b are also included in the transition image 174a. Therefore, the detection of the objects in the images 172a and 174a is a complete detection. The analogy for additional images (e.g., the transition image 174a) incurs a delay penalty.

[0041] An independent analogy is executed for each of the image 172a, the image 172b, and the transition image 174a in FIG. 3. In response to the execution of the analogy, the bounding box 176a is configured on the image 172a, the bounding box 176b is configured on the image 172b, and the bounding box 176c is configured on the transition image 174a. The image 172a includes regions 1, 2, 3, and 4, the image 172b includes region 5, and the transition image 174a includes regions 2, 3, 4, and 5. Since the transition image 174a exists, the object reconstruction module that sacrifices accuracy for speed can accurately detect all the objects in the images 172a and 172b. Even if the object reconstruction module cannot detect region 5 of the object in the image 172b, the pixels originally included in the image 172b are also included in the transition image 174a. Therefore, the detection of the objects in the images 172a and 174a is a complete detection. The analogy for additional images (e.g., the transition image 174a) incurs a delay penalty. The second part of the 72a includes the regions 2, 3, and 4 of the bounding box 176a, and the first part of the image 172b includes the region 5 of the bounding box 176b. In a specific embodiment, the object reconstruction module may generate the transition image 174a to include the second part of the image 172a and the first part of the image 172b by overlaying the second part of the image 172a on the first part of the image 172b. In some embodiments, the transition image 174a is generated by the image capture module. An independent analogy is executed for each of the image 172a, the image 172b, and the transition image 174a in FIG. 3. In response to the execution of the analogy, the bounding box 176a is configured on the image 172a, the bounding box 176b is configured on the image 172b, and the bounding box 176c is configured on the transition image 174a. The image 172a includes regions 1, 2, 3, and 4, the image 172b includes region 5, and the transition image 174a includes regions 2, 3, 4, and 5. Since the transition image 174a exists, the object reconstruction module that sacrifices accuracy for speed can accurately detect all the objects in the images 172a and 172b. Even if the object reconstruction module cannot detect region 5 of the object in the image 172b, the pixels originally included in the image 172b are also included in the transition image 174a. Therefore, the detection of the objects in the images 172a and 174a is a complete detection. The analogy for additional images (e.g., the transition image 174a) incurs a delay penalty. The second part of the 72a includes the regions 2, 3, and 4 of the bounding box 176a, and the first part of the image 172b includes the region 5 of the bounding box 176b. In a specific embodiment, the object reconstruction module may generate the transition image 174a to include the second part of the image 172a and the first part of the image 172b by overlaying the second part of the image 172a on the first part of the image 172b. In some embodiments, the transition image 174a is generated by the image capture module.

[0042] Since the transition image 174a exists, the object reconstruction module that sacrifices accuracy for speed can accurately detect all the objects in the images 172a and 172b. Even if the object reconstruction module cannot detect region 5 of the object in the image 172b, the pixels originally included in the image 172b are also included in the transition image 174a. Therefore, the detection of the objects in the images 172a and 174a is a complete detection. The analogy for additional images (e.g., the transition image 174a) incurs a delay penalty. The second part of the 72a includes the regions 2, 3, and 4 of the bounding box 176a, and the first part of the image 172b includes the region 5 of the bounding box 176b. In a specific embodiment, the object reconstruction module may generate the transition image 174a to include the second part of the image 172a and the first part of the image 172b by overlaying the second part of the image 172a on the first part of the image 172b. In some embodiments, the transition image 174a is generated by the image capture module. An independent analogy is executed for each of the image 172a, the image 172b, and the transition image 174a in FIG. 3. In response to the execution of the analogy, the bounding box 176a is configured on the image 172a, the bounding box 176b is configured on the image 172b, and the bounding box 176c is configured on the transition image 174a. The image 172a includes regions 1, 2, 3, and 4, the image 172b includes region 5, and the transition image 174a includes regions 2, 3, 4, and 5. Since the transition image 174a exists, the object reconstruction module that sacrifices accuracy for speed can accurately detect all the objects in the images 172a and 172b. Even if the object reconstruction module cannot detect region 5 of the object in the image 172b, the pixels originally included in the image 172b are also included in the transition image 174a. Therefore, the detection of the objects in the images 172a and 174a is a complete detection. The analogy for additional images (e.g., the transition image 174a) incurs a delay penalty. The second part of the 72a includes the regions 2, 3, and 4 of the bounding box 176a, and the first part of the image 172b includes the region 5 of the bounding box 176b. In a specific embodiment, the object reconstruction module may generate the transition image 174a to include the second part of the image 172a and the first part of the image 172b by overlaying the second part of the image 172a on the first part of the image 172b. In some embodiments, the transition image 174a is generated by the image capture module. An independent analogy is executed for each of the image 172a, the image 172b, and the transition image 174a in FIG. 3. In response to the execution of the analogy, the bounding box 176a is configured on the image 172a, the bounding box 176b is configured on the image 172b, and the bounding box 176c is configured on the transition image 174a. The image 172a includes regions 1, 2, 3, and 4, the image 172b includes region 5, and the transition image 174a includes regions 2, 3, 4, and 5. It incurs latency, but in certain embodiments, this latency penalty can be less than the latency penalty resulting from accurately detecting region 5 of the object in image 172b. It can be less. The following FIGS. 4 and 5 show additional processing applied to the images to avoid overlap of regions 1 to 5. FIG. 4 shows an exemplary equivalent region 178a that can be determined by the system of FIG. 1. Image 172a includes a bounding box 176a (surrounding region 1, region 2, region 3

[0043] and region 4 of the object), and image 172b includes a bounding box 176b (surrounding region 5 of the object). In certain embodiments, each region can be surrounded by its own bounding box 176. For example, region 1 of image 172a can be surrounded by a first bounding box 176, region 2 of image 172a can be surrounded by a second bounding box 176, and so on. In certain embodiments, the bounding boxes can be nested within each other. For example, if two other overlapping objects are detected, region 1 can be surrounded by a first bounding box 176 that can be nested within a larger bounding box 176. It may be surrounded by a second bounding box 176, and so on. In certain embodiments, the bounding boxes can be nested within each other. For example, if two other overlapping objects are detected, region 1 can be surrounded by a first bounding box 176 that can be nested within a larger bounding box 176. For example, if two other overlapping objects are detected, region 1 can be surrounded by a first bounding box 176 that can be nested within a larger bounding box 176. It may be surrounded.

[0044] As shown in FIG. 4, the bounding box 176a of image 172a and the bounding box 176c of the transition image 174 a are not equivalent. The bounding box 176a and the bounding box 176c occupy different coordinate spaces and include different regions of the object. However, the pixels included in the second portion of image 172a are the same as those in the transition image 174a. They are the same pixels as those included in the first part of the shifted image 174a. The bar of the bounding box 176a of the winding box 176a and the bounding box 176c of the transition image 174a The simple concatenation generates region 12342345, which is an inaccurate duplication of regions 2 , 3, and 4 of the entire object.

[0045] To accurately reconstruct the entire object without overlapping the regions of the object, image 1 72a and / or the transition image 174a are shrunk considering the overlapping pixels. In a specific imp lementation, the object reconstruction module in FIG. 1 determines the equivalent regions 178a in image 172a and the transition image 174 a. The equivalent region 178a is a region in two or more images that share the same pixels. The object reconstruction module 170 may use the IoU, the equivalence of detection labels, and / or the threshold of the confidence score to determine the equivalent region 178a . For example, the object reconstruction module 170 may use the IoU to determine that the bounding box 176 surrounding regions 2, 3, and 4 in image 172a and regions 2, 3, and 4 in the transition image 174a are the equivalent region 178a . In a specific implementation, the object reconstruction module calculates the IoU of the detections in image 172a and the transition image 174a and determines whether the IoU is greater than a predetermined threshold. If the IoU is greater than the predetermined threshold, the object reconstruction module 170 determines that the overlap between image 172a and the transition image 174a defines the equivalent region 178a . As will be described in FIG. 5 below, the equivalent region 178a may be excluded from the final object reconstruction . . If the IoU is greater than the predetermined threshold, the object reconstruction module 170 determines that the overlap between image 172a and the transition image 174a defines the equivalent region 178a . As described in FIG. 5 below, the equivalent region 178a may be excluded from the final object reconstruction .

[0046] ​FIG. 5 shows an exemplary object reconstruction 180a that can be generated by the system of FIG. 1. As shown in FIG. 4 above, regions 2, 3, and 4 of image 172a and transition image 174a are equivalent regions 178a. The equivalent regions 178a are excluded from the object reconstruction 180a (i.e., regions 2, 3, and 4 are included in the object reconstruction 180a only once) to avoid duplication of such regions. The object reconstruction module of FIG. 1 generates the object reconstruction 180a by concatenating regions 1, 2, 3, 4, and 5. The concatenation of regions 1 through 5 can be represented as 12345 such that the object reconstruction 180a represents the object (e.g., the entire object 160 of FIG. 1). If the object reconstruction module detects region 5 of the object within image 172b, the same process can be applied to avoid duplication of equivalent region 178b.

[0047] FIG. 6 shows an exemplary method 600 for reconstructing an object using a transition image (e.g., transition image 174a of FIG. 1). Method 600 begins at step 610. At step 620, an object reconstruction module (e.g., object reconstruction module 170 of FIG. 1) receives two or more images of an object (e.g., object 160 of FIG. 1) via a network (e.g., network 110 of FIG. 1). For example, the object reconstruction module may receive a first image (e.g., image 172a of FIG. 1) and a second image (e.g., image 172b of FIG. 1) of the object from an image capture module (e.g., image capture module 150 of FIG. 1). The image capture module may receive the first and second images of the object. Yule can be attached to a railway vehicle (e.g., railway vehicle 140 in FIG. 1). In a specific implementation form, the image capture module captures an image of an object while the railway vehicle moves along a railway line (e.g., railway line 130) in a railway environment (e.g., railway environment 120). Next, method 600 moves from step 620 to step 630 .

[0048] In step 630 of method 600, the object reconstruction module determines whether to use a transition image (e.g., transition image 174a in FIG. 3) for object reconstruction. For example, the object reconstruction module may determine to use the transition image if the object reconstruction module prioritizes the object detection speed over the object detection accuracy (e.g., detecting a relatively small portion of the object in the received image). If the object reconstruction module determines not to use the transition image for object reconstruction , the object reconstruction module proceeds from step 630 to step 670, and the object reconstruction module generates a reconstruction of the object from the received image. For example, the first image received by the object reconstruction module may include a first region, a second region, and a third region of the object, and the second image received by the object reconstruction module may include a fourth region and a fifth region of the object. The object reconstruction module may detect the first region, the second region, and the third region of the object in the first image. The object reconstruction module may detect the fourth region and the fifth region of the object in the second image.

[0049] The object reconstruction module may detect the first region, the second region, and the third region of the object in the first image. The object reconstruction module may detect the object in the second image The fourth and fifth regions of the object can be detected. The object reconstruction module combines the first region, the second region, and the third region detected in the first image with the fourth region and the fifth region detected in the second image so that the object reconstruction represents the entire object, thereby generating an object reconstruction. Next, method 600 moves from step 670 to step 680, where method 600 ends.

[0050] In step 630, if the object reconstruction module determines to utilize a transition image for object reconstruction, the object reconstruction module can create a transition image by overlapping the first image and the second image such that the transition image includes the overlapping portions of each image. For example, the first image received by the object reconstruction module may include the first region, the second region, the third region, and the fourth region of the object, and the second image received by the object reconstruction module may include the fifth region of the object. The object reconstruction module can generate a transition image by overlapping the first image and the second image such that the transition image includes the second region, the third region, the fourth region, and the fifth region of the object. In a particular embodiment, the object reconstruction module receives the transition image from the image capture module. Next, method 600 moves from step 630 to step 640.

[0051]

[0051] In step 640 of method 600, the object reconstruction module performs inference on each image. For example, the object reconstruction module executes an object detection algorithm to detect the object in the first image, the second image, and the transition image. ​​Analogies may be performed on the first image, the second image, and the transition image. In response to performing the analogy to detect an object, the object reconstruction module may generate one or more bounding boxes around the detected object (or a part thereof). Each bounding box may include one or more regions of the object. Next, method 600 moves from step 640 to step 650. In step 650 of method 600, the object reconstruction module determines an equivalent region (e.g., equivalent region 178a in FIG. 4) in the received image. The equivalent region is a region that shares the same pixels. In certain embodiments, the object reconstruction module may perform an intersection of the bounding boxes and the overlapping regions of the first image, the second image, and the transition image to normalize the bounding boxes that may extend beyond the overlapping region before calculating the IoU of two or more boxes. In some embodiments, the object reconstruction module uses a threshold of IoU, detection label equivalence, and / or confidence score to determine the overlapping region. For example, the object reconstruction module may use a threshold of IoU, detection label equivalence, and / or confidence score to determine that the second region, the third region, and the fourth region overlap in the first image and the transition image. As another example, the object reconstruction module may use a threshold of IoU, equivalence of detection labels, and / or confidence score to determine that the fifth region overlaps in the second image and the transition image. Next, method 600 moves from step 650 to step 660. In response to performing the analogy to detect an object, the object reconstruction module may generate one or more bounding boxes around the detected object (or a part thereof). Each bounding box may include one or more regions of the object. Next, method 600 moves from step 640 to step 650. In step 650 of method 600, the object reconstruction module determines an equivalent region (e.g., equivalent region 178a in FIG. 4) in the received image. The equivalent region is a region that shares the same pixels.

[0052] In step 650 of method 600, the object reconstruction module determines an equivalent region (e.g., equivalent region 178a in FIG. 4) in the received image. The equivalent region is a region that shares the same pixels. In certain embodiments, the object reconstruction module may perform an intersection of the bounding boxes and the overlapping regions of the first image, the second image, and the transition image to normalize the bounding boxes that may extend beyond the overlapping region before calculating the IoU of two or more boxes. In some embodiments, the object reconstruction module uses a threshold of IoU, detection label equivalence, and / or confidence score to determine the overlapping region. For example, the object reconstruction module may use a threshold of IoU, detection label equivalence, and / or confidence score to determine that the second region, the third region, and the fourth region overlap in the first image and the transition image. In certain embodiments, the object reconstruction module may perform an intersection of the bounding boxes and the overlapping regions of the first image, the second image, and the transition image to normalize the bounding boxes that may extend beyond the overlapping region before calculating the IoU of two or more boxes. In some embodiments, the object reconstruction module uses a threshold of IoU, detection label equivalence, and / or confidence score to determine the overlapping region. For example, the object reconstruction module may use a threshold of IoU, detection label equivalence, and / or confidence score to determine that the second region, the third region, and the fourth region overlap in the first image and the transition image. As another example, the object reconstruction module may use a threshold of IoU, equivalence of detection labels, and / or confidence score to determine that the fifth region overlaps in the second image and the transition image. For example, the object reconstruction module may use a threshold of IoU, detection label equivalence, and / or confidence score to determine that the second region, the third region, and the fourth region overlap in the first image and the transition image. As another example, the object reconstruction module may use a threshold of IoU, equivalence of detection labels, and / or confidence score to determine that the fifth region overlaps in the second image and the transition image. Next, method 600 moves from step 650 to step 660. Next, method 600 moves from step 650 to step 660. Next, method 600 moves from step 650 to step 660.

[0053] In step 660 of method 600, the object reconstruction module excludes equivalent regions from the object reconstruction. For example, if the object reconstruction module determines that the second region, the third region, and the fourth region from the first image and the transition image are equivalent regions, the object reconstruction module may exclude the equivalent regions in the object reconstruction so that the object reconstruction includes only one copy of the second region, the third region, and the fourth region. As another example, if the object reconstruction module determines that the fifth region from the second image and the transition image are equivalent regions, the object reconstruction module may exclude the equivalent regions in the object reconstruction so that the object reconstruction includes only one copy of the fifth region. Next, method 600 moves from step 660 to step 670. In step 670 of method 600, the object reconstruction module generates an object reconstruction (e.g., object reconstruction 180a in FIG. 5) from one or more received images and one or more transition images. For example, the first image received by the object reconstruction module may include the first region, the second region, the third region, and the fourth region of the object, and the transition image generated in step 630 of method 600 may include the second region, the third region, the fourth region, and the fifth region of the object. The object reconstruction module may generate an object reconstruction by concatenating the first region, the second region, the third region, and the fourth region from the first image with the fifth region of the transition image so that the object reconstruction shows the entire object. Next, method 600 moves from step 670 to step 680. For example, if the object reconstruction module determines that the second region, the third region, and the fourth region from the first image and the transition image are equivalent regions, the object reconstruction module may exclude the equivalent regions in the object reconstruction so that the object reconstruction includes only one copy of the second region, the third region, and the fourth region. For example, if the object reconstruction module determines that the second region, the third region, and the fourth region from the first image and the transition image are equivalent regions, the object reconstruction module may exclude the equivalent regions in the object reconstruction so that the object reconstruction includes only one copy of the second region, the third region, and the fourth region. For example, if the object reconstruction module determines that the second region, the third region, and the fourth region from the first image and the transition image are equivalent regions, the object reconstruction module may exclude the equivalent regions in the object reconstruction so that the object reconstruction includes only one copy of the second region, the third region, and the fourth region. As another example, if the object reconstruction module determines that the fifth region from the second image and the transition image are equivalent regions, the object reconstruction module may exclude the equivalent regions in the object reconstruction so that the object reconstruction includes only one copy of the fifth region. Next, method 600 moves from step 660 to step 670. As another example, if the object reconstruction module determines that the fifth region from the second image and the transition image are equivalent regions, the object reconstruction module may exclude the equivalent regions in the object reconstruction so that the object reconstruction includes only one copy of the fifth region. As another example, if the object reconstruction module determines that the fifth region from the second image and the transition image are equivalent regions, the object reconstruction module may exclude the equivalent regions in the object reconstruction so that the object reconstruction includes only one copy of the fifth region. As another example, if the object reconstruction module determines that the fifth region from the second image and the transition image are equivalent regions, the object reconstruction module may exclude the equivalent regions in the object reconstruction so that the object reconstruction includes only one copy of the fifth region. As another example, if the object reconstruction module determines that the fifth region from the second image and the transition image are equivalent regions, the object reconstruction module may exclude the equivalent regions in the object reconstruction so that the object reconstruction includes only one copy of the fifth region. Next, method 600 moves from step 660 to step 670.

[0054] In step 670 of method 600, the object reconstruction module generates an object reconstruction (e.g., object reconstruction 180a in FIG. 5) from one or more received images and one or more transition images. In step 670 of method 600, the object reconstruction module generates an object reconstruction (e.g., object reconstruction 180a in FIG. 5) from one or more received images and one or more transition images. For example, the first image received by the object reconstruction module may include the first region, the second region, the third region, and the fourth region of the object, and the transition image generated in step 630 of method 600 may include the second region, the third region, the fourth region, and the fifth region of the object. In step 670 of method 600, the object reconstruction module generates an object reconstruction (e.g., object reconstruction 180a in FIG. 5) from one or more received images and one or more transition images. For example, the first image received by the object reconstruction module may include the first region, the second region, the third region, and the fourth region of the object, and the transition image generated in step 630 of method 600 may include the second region, the third region, the fourth region, and the fifth region of the object. The object reconstruction module may generate an object reconstruction by concatenating the first region, the second region, the third region, and the fourth region from the first image with the fifth region of the transition image so that the object reconstruction shows the entire object. Next, method 600 moves from step 670 to step 680. For example, the first image received by the object reconstruction module may include the first region, the second region, the third region, and the fourth region of the object, and the transition image generated in step 630 of method 600 may include the second region, the third region, the fourth region, and the fifth region of the object. For example, the first image received by the object reconstruction module may include the first region, the second region, the third region, and the fourth region of the object, and the transition image generated in step 630 of method 600 may include the second region, the third region, the fourth region, and the fifth region of the object. The object reconstruction module may generate an object reconstruction by concatenating the first region, the second region, the third region, and the fourth region from the first image with the fifth region of the transition image so that the object reconstruction shows the entire object. Next, method 600 moves from step 670 to step 680. For example, the first image received by the object reconstruction module may include the first region, the second region, the third region, and the fourth region of the object, and the transition image generated in step 630 of method 600 may include the second region, the third region, the fourth region, and the fifth region of the object. The object reconstruction module may generate an object reconstruction by concatenating the first region, the second region, the third region, and the fourth region from the first image with the fifth region of the transition image so that the object reconstruction shows the entire object. Next, method 600 moves from step 670 to step 680. The object reconstruction module may generate an object reconstruction by concatenating the first region, the second region, the third region, and the fourth region from the first image with the fifth region of the transition image so that the object reconstruction shows the entire object. Next, method 600 moves from step 670 to step 680. The object reconstruction module may generate an object reconstruction by concatenating the first region, the second region, the third region, and the fourth region from the first image with the fifth region of the transition image so that the object reconstruction shows the entire object. Next, method 600 moves from step 670 to step 680. The object reconstruction module may generate an object reconstruction by concatenating the first region, the second region, the third region, and the fourth region from the first image with the fifth region of the transition image so that the object reconstruction shows the entire object. Next, method 600 moves from step 670 to step 680. Move and method 600 ends here. Thus, method 600 can be used to overall reconstruct an object even if a specific area of the object in the received image is not detected. Even if a specific area of the object is not detected, it can be used to overall reconstruct the object. For method 600 shown in FIG. 4, modifications, additions, or omissions can be made. Method 600 can include more, fewer, or other steps. For example, method 600 can include using a second transition image. The steps can be executed in parallel or in any suitable order. Although specific components have been discussed as completing the steps of method 600, any suitable components can execute any step of method 600. For example, one or more steps of method 600 can be executed by an image capture module.

[0055] For method 600 shown in FIG. 4, modifications, additions, or omissions can be made. Method 600 can include more, fewer, or other steps. For example, method 600 can include using a second transition image. The steps can be executed in parallel or in any suitable order. Although specific components have been discussed as completing the steps of method 600, any suitable components can execute any step of method 600. For example, one or more steps of method 600 can be executed by an image capture module. Although specific components have been discussed as completing the steps of method 600, any suitable components can execute any step of method 600. For example, one or more steps of method 600 can be executed by an image capture module.

[0056] FIG. 7 shows an exemplary computer system that can be used by the systems and methods described herein. For example, one or more components of system 100 in FIG. 1 (e.g., image capture module 150 and / or object reconstruction module 170) can include one or more interfaces 710, processing circuitry 720, memory 730, and / or other suitable elements. Interface 710 receives inputs, transmits outputs, processes inputs and / or outputs, and / or performs other suitable operations. Interface 710 can include hardware and / or software. Although specific components have been discussed as completing the steps of method 600, any suitable components can execute any step of method 600. For example, one or more steps of method 600 can be executed by an image capture module. One or more components of system 100 in FIG. 1 (e.g., image capture module 150 and / or object reconstruction module 170) can include one or more interfaces 710, processing circuitry 720, memory 730, and / or other suitable elements. Interface 710 receives inputs, transmits outputs, processes inputs and / or outputs, and / or performs other suitable operations. Interface 710 can include hardware and / or software. Interface 710 receives inputs, transmits outputs, processes inputs and / or outputs, and / or performs other suitable operations.

[0057] Processing circuitry 720 executes or manages the processing of components. Processing circuitry 720 can include hardware and / or software. Examples of processing circuitry are one or more computers, one or more processors, one or more microprocessors, one or more microcontrollers, one or more digital signal processors (DSPs), one or more field programmable gate arrays (FPGAs), and / or other suitable processing components. Memory 730 stores data and / or instructions. Memory 730 can include hardware and / or software. Examples of memory are random access memory (RAM), read only memory (ROM), hard disk drive (HDD), solid state drive (SSD), flash memory, and / or other suitable memory components. ​including one or more microprocessors, one or more applications, etc. Specific embodiments In some embodiments, processing circuitry 720 performs actions (e.g., processing) such as generating an output from an input by executing logic (e.g., instructions). The logic executed by processing circuitry 720 may be encoded on one or more tangible, non-transitory computer-readable media (e.g., memory 730 ). For example, the logic may include a computer program, software, computer-executable instructions, and / or instructions executable by a computer . In certain embodiments, the processing of the embodiment may be performed by one or more computer-readable media that store, implement and / or encode, and / or have a stored and / or encoded computer program .

[0058] Memory 730 (or memory unit) stores information. Memory 730 may include one or more non-transitory, tangible, computer-readable, and / or computer-executable storage media . Examples of memory 730 include computer memory (e.g., RAM or ROM), mass storage media (e.g., hard disk), removable storage media (e.g., compact disc (CD) or digital video disc (DVD)), databases, and / or network work storage (e.g., server) and / or other computer-readable media .

[0059] As used herein, a computer-readable non-transitory storage medium or media includes one or more semiconductor-based substrates or other integrated circuits (ICs) (e.g., field programmable gate array (FPGA A) or application specific integrated circuit (ASIC)), hard disk drive (HDD), ​​​Hybrid hard drive (HHD), optical disk, optical disk drive (ODD), optical magnetic disk, magneto-optical drive, floppy disk, floppy disk drive (F DD), magnetic tape, solid state drive (SSD), RAM drive, SD (S ECURE DIGITAL) card or drive, or other suitable computer-readable non-transitory storage medium, or may include any suitable combination of two or more of these as necessary. The computer-readable non-transitory storage medium may, as necessary, be volatile, non-volatile, or a combination of volatile and non- volatile.

[0060] As used herein, "or" is inclusive and not exclusive unless explicitly stated otherwise or the context indicates otherwise. Accordingly, as used herein, "A or B" means "A, B, or both" unless explicitly stated otherwise or the context indicates otherwise. Also, "and" means joint and plural unless explicitly stated otherwise or the context indicates otherwise. Accordingly, as used herein, "A and B" means "A and B, jointly, or individually" unless explicitly stated otherwise or the context indicates otherwise. The scope of the present disclosure includes all changes, substitutions, transformations, variations, and modifications to the exemplary embodiments described or illustrated herein that would be understood by one of ordinary skill in the art. The scope of the present disclosure is not limited to the exemplary embodiments described or illustrated herein. Further, although each embodiment of the present disclosure is described and illustrated as including a particular component, element, feature, function, process, or step, none of these embodiments are limited to only those components, elements, features, functions, processes, or steps described or illustrated anywhere in this specification that would be understood by one of ordinary skill in the art. The scope of the present disclosure includes all changes, substitutions, transformations, variations, and modifications to the exemplary embodiments described or illustrated herein that would be understood by one of ordinary skill in the art. The scope of the present disclosure is not limited to the exemplary embodiments described or illustrated herein. Further, the present disclosure describes and illustrates each embodiment herein as including a particular component, element, feature, function, process, or step, but none of these embodiments are limited to only those components, elements, features, functions, processes, or steps described or illustrated anywhere in this specification that would be understood by one of ordinary skill in the art. embodiments described or illustrated herein. Any combination of the components, elements, features, functions, processes, or steps described herein. Furthermore, it may include any permutation of the functions that are suitably configured, arranged, operated or The appended claims are directed to an operable device, system, or component thereof. References to the device, system, or component are intended to mean that the device, system, or component is properly configured, arranged, and in operation or working condition. The system or the device may be operated independently of whether a specific function is activated or deactivated. Additionally, the present disclosure may highlight certain embodiments as providing certain advantages. Although described or illustrated, a particular embodiment may not provide all or some of these advantages. The Company may provide part or all of the

Claims

1. An apparatus comprising: one or more processors; one or more computer-readable non-transitory storage media coupled to the one or more processors, the one or more computer-readable non-transitory storage media including instructions that, when executed by the one or more processors, cause the apparatus to perform a process; wherein the process includes: identifying, via a processor, a transition image that includes a first region of an object within a first image of a transportation infrastructure component, and a second region of the object within a second image of the transportation infrastructure component; generating a reconstruction of the object using the transition image, the reconstruction of the object including the first region of the object and the second region of the object; identifying a first portion of the object within the first image; constructing a first bounding box around the first portion of the object within the first image, the first bounding box including the first region of the object; identifying a second portion of the object within the transition image; constructing a second bounding box around the second portion of the object within the transition image, the second bounding box including the first region of the object and the second region of the object within the transition image; An apparatus.

2. The apparatus of claim 1, wherein the first image further includes a third region of the object, a fourth region of the object, and a fifth region of the object, and the transition image includes the third region of the object and the fourth region of the object.

3. The process of claim 2, further including: determining that a third region of the object within the transition image and a third region of the object within the first image are third equivalent regions; and determining that a fourth region of the object within the transition image and a fourth region of the object within the first image are fourth equivalent regions, wherein the reconstruction of the object excludes the third and fourth equivalent regions.

4. ​ ​ ​ ​ The process further includes a process of connecting a first region of the object, a second region of the object, a third region of the object, a fourth region of the object, and a fifth region of the object in order to generate a reconstruction of the object, and the reconstruction of the object represents the entire object, the apparatus according to claim 2.

5. The process further includes a process of using IoU (Intersection over Union) to determine that a first region of the object in the transition image and a first region of the object in the first image are a plurality of first equivalent regions, the apparatus according to claim 1.

6. The one or more processors include an object reconstruction module, the apparatus according to claim 1.

7. The transition image includes half of the first image and half of the second image, the apparatus according to claim 1.

8. A method, identifying a transition image via a processor, the transition image including a first region of an object in a first image of a transportation infrastructure component, and a second region of the object in a second image of the transportation infrastructure component, the step including; a process of generating a reconstruction of the object using the transition image, the reconstruction of the object including the first region of the object and the second region of the object, the step including; identifying a first portion of the object in the first image, constructing a first bounding box around the first portion of the object in the first image, the first bounding box including the first region of the object, the step including; identifying a second portion of the object in the transition image, constructing a second bounding box around the second portion of the object in the transition image, the second bounding box including the first region of the object and the second region of the object in the transition image, the step including; including a method.

9. The first image further includes a third region of the object, a fourth region of the object, and a fifth region of the object, The transition image includes the third region of the object and the fourth region of the object, the method according to claim 8. Step of determining that a third region of the object in the transition image and a third region of the object in the first image are third equivalent regions; Step of determining that a fourth region of the object in the transition image and a fourth region of the object in the first image are fourth equivalent regions; further comprising: The reconstruction of the object excludes the third and fourth equivalent regions. The method according to claim 9.

11. The method further comprises the step of connecting a first region of the object, a second region of the object, a third region of the object, a fourth region of the object, and a fifth region of the object to generate a reconstruction of the object, The reconstruction of the object represents the entire object. The method according to claim 9.

12. The method according to claim 8, further comprising the step of using Intersection over Union (IoU) to determine that a first region of the object in the transition image and a first region of the object in the first image are a plurality of first equivalent regions.

13. The method according to claim 8, wherein the processor includes an object reconstruction module.

14. The method according to claim 8, wherein the transition image includes half of the first image and half of the second image.

15. One or more computer-readable non-transitory storage media that embody instructions for causing the processor to perform processing when executed by the processor, The processing is a process of identifying a transition image via a processor, the transition image including a first region of an object in a first image of a transportation infrastructure component, and a second region of the object in a second image of the transportation infrastructure component, and a process of generating a reconstruction of the object using the transition image, the reconstruction of the object including the first region and the second region of the object, a process of identifying a first portion of the object in the first image, a process of constructing a first bounding box around the first portion of the object in the first image, the first bounding box including the first region of the object, a process of identifying a second portion of the object in the transition image, ​ A process of constructing a second bounding box around the periphery of the second part of the object in the transition image, wherein the second bounding box includes the first region of the object and the second region of the object in the transition image, including a computer-readable non-transitory storage medium. **Claim 16** The first image further includes a third region of the object, a fourth region of the object, and a fifth region of the object, The transition image includes the third region of the object and the fourth region of the object, The computer-readable non-transitory storage medium according to claim 15. **Claim 17** The process includes a process of determining that the third region of the object in the transition image and the third region of the object in the first image are third equivalent regions, and a process of determining that the fourth region of the object in the transition image and the fourth region of the object in the first image are fourth equivalent regions, further including The reconstruction of the object excludes the third and fourth equivalent regions. The computer-readable non-transitory storage medium according to claim 16. **Claim 18** The process further includes a process of connecting the first region of the object, the second region of the object, the third region of the object, the fourth region of the object, and the fifth region of the object to generate a reconstruction of the object, The reconstruction of the object represents the entire object. The computer-readable non-transitory storage medium according to claim 16. **Claim 19** The process further includes a process of using IoU (Intersection over Union) to determine that the first region of the object in the transition image and the first region of the object in the first image are a plurality of first equivalent regions, the computer-readable non-transitory storage medium according to claim 15. **Claim 20** The one or more processors include an object reconstruction module, the computer-readable non-transitory storage medium according to claim 15.

Images