Storage location detection method, storage location detection device, electronic device, vehicle, and medium

Abstract

The present disclosure relates to a storage location detection method, a storage location detection device, an electronic device, a vehicle and a medium. The method comprises: acquiring a target image; detecting the target image to obtain a storage location detection result, wherein the storage location detection result comprises: two entrance corner points and two side edge angles, the side edge angles representing the size of the angle formed by the corresponding side edge of the storage location and the line connecting the two entrance corner points; and determining the position of the storage location according to the storage location detection result. Thus, in the storage location detection, the entrance corner points and the side edge angles are predicted, so that the side edge detection effect is improved in the case where the side edge of the storage location is blocked by objects such as walls, columns, vehicles and the like, and the accuracy and stability of the storage location detection are ensured.

Description

Technical Field

[0001] This disclosure relates to the field of autonomous driving technology, and in particular to a parking space detection method, parking space detection device, electronic device, vehicle, and medium. Background Technology

[0002] With vehicles becoming increasingly intelligent, automatic parking has become a standard feature in most smart electric vehicles. It can meet users' needs for automatic parking, eliminating the need for manual control by the driver during the parking process. Among these features, the detection of parking spaces is an important component. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides a storage location detection method, storage location detection device, electronic equipment, vehicle, and medium.

[0004] According to a first aspect of the present disclosure, a storage location detection method is provided, comprising:

[0005] Acquire the target image;

[0006] The target image is detected to obtain the storage location detection result, wherein the storage location detection result includes: two entrance corner points and two side angles, wherein the side angles represent the size of the angle formed by the line connecting the side of the storage location and the two entrance corner points;

[0007] The location of the storage location is determined based on the storage location detection results.

[0008] In some embodiments, detecting the target image to obtain the location detection result includes:

[0009] The target image is detected using a storage location detection model, wherein the storage location detection model is trained in advance based on sample images and the annotation information of the sample images.

[0010] In some embodiments, the storage location detection result further includes: the direction of entry into the storage area;

[0011] Determining the location of the storage location based on the storage location detection result includes:

[0012] Determine whether to adjust the angles of the two sides;

[0013] In response to determining that the two side angles should be adjusted, the two side angles are adjusted according to the storage direction;

[0014] The location of the storage location is determined based on the two entrance corner points and the adjusted two side angles.

[0015] In some embodiments, determining whether to adjust the two side angles includes:

[0016] Calculate the target angle corresponding to the first included angle between the straight lines containing each of the said side angles;

[0017] In response to the target angle being greater than or equal to a preset angle threshold, it is determined that the angles of the two sides will be adjusted.

[0018] In some embodiments, adjusting the two side angles according to the storage direction includes:

[0019] Calculate the size of the second angle between each of the aforementioned side angles and the direction of entry into the warehouse;

[0020] Adjust the larger of the two side angles corresponding to the second included angle based on the direction of entry into the warehouse.

[0021] In some embodiments, adjusting the larger of the two side angles corresponding to the second included angle based on the warehousing direction includes:

[0022] Adjust the larger of the two side angles to make the side corresponding to that side angle move closer to the storage direction.

[0023] According to a second aspect of the present disclosure, a warehouse location detection device is provided, comprising:

[0024] The acquisition module is configured to acquire the target image;

[0025] The detection module is configured to detect the target image to obtain the storage location detection result, wherein the storage location detection result includes: two entrance corner points and two side angles, wherein the side angles represent the size of the first angle formed by the straight line containing the side of the storage location and the straight line connecting the two entrance corner points;

[0026] The determination module is configured to determine the location of the storage location based on the storage location detection result.

[0027] According to a third aspect of the present disclosure, an electronic device is provided, comprising:

[0028] First processor;

[0029] A first memory for storing instructions executable by a first processor;

[0030] The first processor is configured to run the executable instructions to implement the steps of the warehouse location detection method provided in any embodiment of the first aspect of this disclosure.

[0031] According to a fourth aspect of the present disclosure, a vehicle is provided comprising:

[0032] Second processor;

[0033] A second memory used to store instructions executable by a second processor;

[0034] The second processor is configured to run the executable instructions to implement the steps of the warehouse location detection method provided in any embodiment of the first aspect of this disclosure.

[0035] According to a fifth aspect of the present disclosure, a computer-readable storage medium is provided, having stored thereon computer program instructions that, when executed by a processor, implement the steps of the warehouse location detection method provided in any embodiment of the first aspect of the present disclosure.

[0036] The technical solutions provided by the embodiments of this disclosure can include the following beneficial effects: acquiring a target image; detecting the target image to obtain storage location detection results, wherein the storage location detection results include: two entrance corner points and two side angles, the side angles representing the size of the angle formed by the lines connecting the side of the storage location and the two entrance corner points; determining the location of the storage location based on the storage location detection results. Therefore, predicting the entrance corner points and side angles in storage location detection improves the side detection effect when the side of the storage location is obstructed by objects such as walls, pillars, or vehicles, ensuring the accuracy and stability of storage location detection.

[0037] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0038] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0039] Figure 1 This is a flowchart illustrating a storage location detection method according to an exemplary embodiment.

[0040] Figure 2 This is a schematic diagram illustrating a target image according to an exemplary embodiment.

[0041] Figure 3 This is a schematic diagram illustrating another target image according to an exemplary embodiment.

[0042] Figure 4 This is a flowchart illustrating another method for detecting storage locations according to an exemplary embodiment.

[0043] Figure 5 This is a flowchart of one implementation method of step S230 in the present disclosure.

[0044] Figure 6 This is a flowchart of one implementation method of step S231 in the present disclosure.

[0045] Figure 7 This is a flowchart of one implementation method of step S232 in the present disclosure.

[0046] Figure 8 This is a schematic diagram illustrating yet another target image according to an exemplary embodiment.

[0047] Figure 9 This is a flowchart illustrating yet another method for detecting storage locations according to an exemplary embodiment.

[0048] Figure 10 This is a block diagram illustrating a storage location detection device according to an exemplary embodiment.

[0049] Figure 11 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment.

[0050] Figure 12 This is a block diagram illustrating a vehicle 3700 according to an exemplary embodiment.

[0051] Figure 13 This is a block diagram illustrating an apparatus 1900 according to an exemplary embodiment. Detailed Implementation

[0052] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0053] It should be noted that all actions involving the acquisition of signals, information, or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where the application is located, and with the authorization granted by the owner of the relevant device.

[0054] Figure 1 This is a flowchart illustrating a storage location detection method according to an exemplary embodiment. For example... Figure 1 As shown, the method includes the following steps.

[0055] Step S110: Obtain the target image.

[0056] This involves acquiring the target image to be detected.

[0057] In some embodiments, the target image is a bird's-eye view (BEV); wherein, visual information acquired by multiple image acquisition devices, such as cameras, radar and sensors, is converted into a bird's-eye view through inverse perspective mapping (IPM) and represented based on a top view to obtain the bird's-eye view; the bird's-eye view can provide a wider field of view, more intuitively show the surrounding environment of the observed object (such as a vehicle), and can avoid the scale and occlusion problems of a single directly acquired image perspective.

[0058] In some embodiments, the target image is a bird's-eye view with the vehicle as the observed object, or the target image is a bird's-eye view obtained based on image information acquired by an image acquisition device of the vehicle.

[0059] Step S120: Detect the target image to obtain the location detection result.

[0060] The term "garage space" can also be referred to as "parking space," but its name is not limited to this. The parking space detection result includes two entrance corner points and two side angles. The side angle represents the size of the angle formed by the line connecting the side of the parking space and the two entrance corner points. The vertex of the angle is an entrance corner point, one side is the line connecting the two entrance corner points, and the other side is the side of the parking space. Thus, based on an entrance corner point and its corresponding side angle, the direction of one side of the parking space can be determined.

[0061] Among them, corner points can be defined as the intersection of two lines, the connection point of the outline of an object, and the junction of two edge lines of an object; the entrance corner point represents the position of the connection point between one side of the storage location and the entrance line of the storage location. The two entrance corner points in the storage location detection result represent the position of the entrance line. The line connecting the two entrance corner points is the storage location entrance line obtained based on the storage location detection result.

[0062] In some embodiments, the side angle represents the size of the angle between the line containing the side of the storage location and the line connecting the two entrance corner points, wherein the angle is the smallest positive angle formed by the intersection of two lines (or vectors).

[0063] Step S130: Determine the location of the storage location based on the storage location detection results.

[0064] The location of the parking space can be determined based on the two entrance corner points and two side angles mentioned above. The determined location of the parking space can then be applied to scenarios such as automatic parking.

[0065] Figure 2This is a schematic diagram illustrating a target image according to an exemplary embodiment. For example... Figure 2 As shown, the target image is a bird's-eye view acquired with vehicles as the observed object. The black rectangles in the image represent the empty areas formed after stitching together directly acquired images to obtain the bird's-eye view. Dashed boxes represent storage locations, and solid boxes filled with diagonal lines represent obstructions such as walls, pillars, or vehicles. The overlapping portion of the dashed and solid boxes indicates the obstructed part of the storage location. For ease of observation and description, the solid boxes have a certain degree of transparency; it is understood that the obstructed parts of the storage locations are not visible in the target image during actual observation. In this target image, several storage locations on the left are clear and unobstructed, while some storage locations on the right are obstructed. The rear corners of the obstructed storage locations are not visible in the target image. In related technologies, it is necessary to predict the four corners of the storage location: the two entrance corners and the two rear corners. However, if the two rear corners of the storage location are obstructed in the target image, the accuracy of the storage location detection results cannot be guaranteed. Furthermore, the entrance corner of the parking space is less likely to be obscured and is more prominent in the target image because it is closer to the vehicle's image acquisition device among the four corner points.

[0066] Figure 3 This is a schematic diagram illustrating another target image according to an exemplary embodiment. After performing location detection on the target image, see [link to diagram]. Figure 3 It shows the results of determining the location of the storage location based on the storage location detection results; for example, hollow circles A and A' represent the detected entrance corner points, the two detected side angles correspond to ∠1 and ∠2 respectively, solid lines with arrows represent the directions of the sides corresponding to ∠1 and ∠2, and dashed boxes represent the storage locations determined based on the storage location detection results.

[0067] This disclosure provides a storage location detection method, which includes: acquiring a target image; detecting the target image to obtain storage location detection results, wherein the storage location detection results include: two entrance corner points and two side angles, the side angles representing the angles formed by the lines connecting the side of the storage location to the two entrance corner points; and determining the location of the storage location based on the storage location detection results. Therefore, predicting the entrance corner points and side angles in storage location detection improves the side detection effect when the side of the storage location is obstructed by objects such as walls, pillars, or vehicles, ensuring the accuracy and stability of storage location detection. Furthermore, compared to related technologies that detect four corner points, the solution provided by this disclosure achieves more stable and accurate detection results without increasing additional memory and computational overhead.

[0068] Figure 4 This is a flowchart illustrating another method for detecting storage locations according to an exemplary embodiment. For example... Figure 4As shown, the method includes the following steps.

[0069] Step S210: Obtain the target image.

[0070] In some embodiments, the target image is a bird's-eye view.

[0071] Step S221: Detect the target image using the location detection model.

[0072] The storage location detection results obtained by detecting the target image include two entrance corner points and two side angles. The side angles represent the size of the angle formed by the lines connecting the side of the storage location and the two entrance corner points.

[0073] The storage location detection model is pre-trained based on sample images and their annotation information. For example, the sample images are used as input to the model, and the annotation information is used as the target output. The model is trained based on the difference between the actual output value and the annotation information. In some embodiments, the storage location detection model is obtained in response to a preset model training stopping condition. This stopping condition may include: the model error reaching a preset error threshold, or the training step size reaching a preset maximum step size.

[0074] The annotation information can be used to characterize the positions of the two entrance corners of the storage location in the sample image, and to characterize the side angles corresponding to the two sides of the storage location or the positions of the two sides in the sample image. It can be used to describe the truth labeling results after truth labeling of relevant information in the sample image.

[0075] In some embodiments, the sample image is a bird's-eye view.

[0076] In some embodiments, the storage location detection model is based on a convolutional neural network.

[0077] Step S230: Determine the location of the storage location based on the storage location detection results.

[0078] Therefore, based on the solution provided by the embodiments of this disclosure, by learning and predicting the entrance corner point and side angle, the adverse effects of the corner points that are obscured or invisible at the back of the storage location on the model learning and detection process are avoided. At the same time, it also implicitly makes the model learning and detection process more focused on the unobscured part of the side of the storage location, extracting information from the limited unobscured area at the front of the storage location to obtain accurate entrance corner points and side angles, thereby improving the accuracy of storage location detection.

[0079] Figure 5 This is a flowchart illustrating one implementation method of step S230 in this disclosure. The storage location detection result also includes the storage direction; such as... Figure 5As shown, step S230, determining the location of the storage location based on the storage location detection result, includes steps S231 to S233.

[0080] Step S231: Determine whether to adjust the angles of the two sides.

[0081] In order to prevent large errors in the side angles obtained by the detection, it is determined whether at least one of the two side angles should be adjusted.

[0082] In some embodiments, when the side of the storage location is mostly or even completely obscured in the target image, the detected side angle may deviate too much from the actual value. In such cases, it is necessary to constrain the predicted side to ensure that the error of the side angle is within an acceptable range.

[0083] Step S232: In response to determining to adjust the two side angles, adjust the two side angles according to the storage direction.

[0084] The entry direction represents the direction and angle at which the vehicle enters the parking space. In some embodiments, the angle formed by the entry direction and the lines connecting the two entry corners is a preset angle or falls within a preset angle range.

[0085] The annotation information can be used not only to characterize the positions of the two entrance corners of the storage location in the sample image, and the side angles corresponding to the two sides of the storage location or the positions of the two sides in the sample image, but also to characterize the storage location's entry direction. During the training process of the storage location detection model, it learns the entrance corners, side angles, and entry direction.

[0086] Step S233: Determine the location of the storage location based on the two entrance corner points and the adjusted two side angles.

[0087] It is understood that the above implementation method also applies to step S130.

[0088] Figure 6 This is a flowchart illustrating one implementation method of step S231 in this disclosure. Figure 6 As shown, step S231 determines whether to adjust the angles of the two sides, including steps S2311 and S2312.

[0089] Step S2311: Calculate the target angle corresponding to the first included angle between the straight lines containing the sides corresponding to each side angle.

[0090] Specifically, for each side angle, the target angle corresponding to the straight line containing the side is calculated.

[0091] Step S2312: In response to the target angle being greater than or equal to a preset angle threshold, determine to adjust the angles of the two sides.

[0092] The angle threshold can be set according to the actual situation. If it is greater than or equal to the angle threshold, it means that the deviation between the two sides is too large and does not meet the actual characteristics of the storage location.

[0093] In some embodiments, in response to a target angle being less than a preset angle threshold, the location of the storage location can be determined based on the two entrance corner points and the two side angles.

[0094] Figure 7 This is a flowchart illustrating one implementation method of step S232 in this disclosure. Figure 7 As shown, step S232 involves adjusting the angles of the two sides according to the direction of entry into the warehouse, including steps S2321 and S2312.

[0095] Step S2321: Calculate the size of the second angle between the side and the direction of entry for each side angle.

[0096] Specifically, for each side angle, the size of the second included angle between the two sides and the direction of entry is calculated.

[0097] Step S2322: Adjust the two side angles according to the direction of entry into the warehouse, and choose the larger of the two side angles.

[0098] Among them, the second included angle corresponding to each side angle is compared, and the larger of the corresponding second included angles is adjusted according to the direction of entry into the warehouse.

[0099] Among the two side angles, the side with the larger second included angle has a greater deviation from the input direction. The side with this angle is more likely to be occluded in the target image, making the side angle unreliable. Therefore, it needs to be adjusted according to the input direction.

[0100] In some embodiments, the larger of the two side angles corresponding to the second included angle is adjusted so that the side corresponding to that side angle moves closer to the storage direction.

[0101] In some embodiments, the two side angles are adjusted based on the idea of ​​majority voting to constrain the predicted side. Specifically, corresponding to steps S2311, S2312, S2321, and S2322, a decision is made based on the first included angle and the second included angle for the direction of the side corresponding to the two side angles and the warehousing direction, thereby determining whether to adjust the two side angles so that the side corresponding to one of them moves closer to the warehousing direction, thus constraining the predicted side.

[0102] In some embodiments, the side angle represents the angle between the straight line containing the side of the storage location and the straight line connecting the two entrance corner points. The side angle can be increased to make the corresponding side move closer to the storage direction.

[0103] Figure 8 This is a schematic diagram illustrating yet another target image according to an exemplary embodiment. After performing location detection on the target image, see [link to diagram]. Figure 8 The diagram shows the result of adjusting the side angles in the storage location detection results; for example, in (1), hollow circles B and B' represent the detected entrance corner points, the side angle before adjustment corresponds to ∠3, and the direction of the side corresponding to ∠3 is represented by a dotted line with an arrow, the two side angles after adjustment correspond to ∠4 and ∠5 respectively, the direction of the side corresponding to ∠4 and ∠5 is represented by a solid line with an arrow, the direction of entry is represented by a dotted line with an arrow, and the dashed box indicates the direction of entry based on the entrance corner point. The storage location is determined by the adjusted side angle; in (2), hollow circles C and C' represent the detected entrance corner points. The side angle before adjustment corresponds to ∠6. The direction of the side corresponding to ∠6 is represented by a line composed of dots with arrows. The two side angles after adjustment correspond to ∠7 and ∠8 respectively. The direction of the side corresponding to ∠7 and ∠8 is represented by a solid line with arrows. The direction of entering the storage is represented by a dotted line with arrows. The dashed box represents the storage location determined by the entrance corner point and the adjusted side angle.

[0104] Therefore, during the training process of the storage location detection model, it learns the entrance corner point, side angle, and entry direction, and corrects the side angle based on the entry direction to ensure the side detection effect of the storage location. It also avoids the situation where the predicted side angle deviates too much from the actual value when most or even all of the side of the storage location is blocked.

[0105] The storage location detection method provided in this disclosure is described below in conjunction with practical applications.

[0106] Figure 9 This is a flowchart illustrating yet another storage location detection method according to an exemplary embodiment. For example... Figure 9 As shown, the method includes the following steps.

[0107] Step S310: Obtain the target image.

[0108] The target image is a bird's-eye view, which is obtained from image information captured by image acquisition devices installed on the vehicle, such as surround-view cameras.

[0109] Step S321: Use the location detection model to detect the target image.

[0110] The storage location detection model is trained in advance based on sample images and annotation information. The storage location detection results include two entrance corner points, two side angles, and the direction of entry.

[0111] Step S3311: Calculate the target angle corresponding to the first included angle between the straight lines containing the sides corresponding to each side angle.

[0112] Step S3312: Determine whether the target angle is greater than or equal to the preset angle threshold.

[0113] In step S3312, if the target angle is greater than or equal to the preset angle threshold, then it is determined that the two side angles will be adjusted and step S3321 is executed; if the target angle is less than the preset angle threshold, then step S3331 is executed.

[0114] Step S3331: Determine the location of the storage location based on the two entrance corner points and the two side angles.

[0115] Step S3321: Calculate the size of the second angle between the side and the direction of entry for each side angle.

[0116] Step S3322: Adjust the two side angles according to the direction of entry into the warehouse, and choose the larger of the two side angles.

[0117] Step S3332: Adjust the larger of the two side angles to make the side corresponding to that side angle move closer to the storage direction.

[0118] The optional implementation methods and related terminology explanations for the above steps can be found in the optional implementation methods of the aforementioned steps and other related parts of the embodiments involved in the relevant steps, which will not be repeated here.

[0119] Figure 10 This is a block diagram illustrating a warehouse location detection device according to an exemplary embodiment. Figure 10 As shown, the storage location detection device 70 includes: an acquisition module 71, a detection module 72, and a determination module 73.

[0120] The acquisition module 71 is configured to acquire the target image.

[0121] The detection module 72 is configured to detect the target image to obtain the storage location detection result, wherein the storage location detection result includes: two entrance corner points and two side angles, and the side angles represent the size of the angle formed by the lines connecting the side of the storage location and the two entrance corner points.

[0122] The determination module 73 is configured to determine the location of the storage location based on the storage location detection results.

[0123] In some embodiments, the detection module 72 is configured to detect the target image using a storage location detection model, wherein the storage location detection model is trained in advance based on sample images and annotation information.

[0124] In some embodiments, the storage location detection result further includes the inbound direction; the determination module 73 is configured to determine whether to adjust the two side angles; in response to determining that the two side angles should be adjusted, the two side angles are adjusted according to the inbound direction; and the location of the storage location is determined according to the two entrance corner points and the adjusted two side angles.

[0125] In some embodiments, the determining module 73 is configured to calculate the target angle corresponding to the first included angle between the straight lines where the sides are located, and to determine to adjust the two side angles in response to the angle corresponding to the first included angle being greater than or equal to a preset angle threshold.

[0126] In some embodiments, the determining module 73 is configured to calculate the size of the second included angle between the side corresponding to each side angle and the warehousing direction; and adjust the larger of the two side angles and the corresponding second included angle according to the warehousing direction.

[0127] In some embodiments, the determining module 73 is configured to adjust the larger of the two side angles corresponding to the second included angle, so that the side corresponding to the side angle moves closer to the storage direction.

[0128] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0129] This disclosure also provides a vehicle that includes the aforementioned warehouse location detection device.

[0130] This disclosure also provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the steps of the warehouse location detection method provided in any of the above embodiments.

[0131] Figure 11 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 may be a computer, an in-vehicle terminal, a mobile phone, a digital broadcasting terminal, a messaging device, a tablet device, a personal digital assistant, etc.

[0132] Reference Figure 11The electronic device 800 may include one or more of the following components: a first processing component 802, a first memory 804, a first power supply component 806, a multimedia component 808, an audio component 810, a first input / output interface 812, a sensor component 814, and a communication component 816.

[0133] The first processing component 802 typically controls the overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. The first processing component 802 may include one or more first processors 820 to execute instructions to perform all or part of the steps of the storage location detection method provided in any of the above embodiments. Furthermore, the first processing component 802 may include one or more modules to facilitate interaction between the first processing component 802 and other components. For example, the first processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the first processing component 802.

[0134] The first memory 804 is configured to store various types of data to support the operation of the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. The first memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0135] The first power supply component 806 provides power to various components of the electronic device 800. The first power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0136] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0137] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in first memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0138] The first input / output interface 812 provides an interface between the first processing component 802 and the peripheral interface module, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to, a home button, volume buttons, a start button, and a lock button.

[0139] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 can detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0140] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0141] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the warehouse location detection method provided in any of the above embodiments.

[0142] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a first memory 804 including instructions, which can be executed by a first processor 820 of an electronic device 800 to complete the storage location detection method provided in any of the above embodiments. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0143] Figure 12 This is a block diagram illustrating a vehicle 3700 according to an exemplary embodiment. For example, vehicle 3700 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicle. Vehicle 3700 may be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle.

[0144] Reference Figure 12 The vehicle 3700 may include various subsystems, such as an infotainment system 3710, a perception system 3720, a decision control system 3730, a drive system 3740, and a computing platform 3750. The vehicle 3700 may also include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and component of the vehicle 3700 can be interconnected via wired or wireless means.

[0145] In some embodiments, the infotainment system 3710 may include a communication system, an entertainment system, and a navigation system, etc.

[0146] The perception system 3720 may include several sensors for sensing information about the environment surrounding the vehicle 3700. For example, the perception system 3720 may include a global positioning system (which may be a GPS system, a BeiDou system, or another positioning system), an inertial measurement unit (IMU), lidar, millimeter-wave radar, ultrasonic radar, and a camera device.

[0147] The decision control system 3730 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0148] The drive system 3740 may include components that provide powered motion to the vehicle 3700. In one embodiment, the drive system 3740 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of internal combustion engines, electric motors, and compressed air engines. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0149] Some or all of the functions of the vehicle 3700 are controlled by a computing platform 3750. The computing platform 3750 may include at least one second processor 3751 and a second memory 3752, the second processor 3751 being able to execute instructions 3753 stored in the second memory 3752.

[0150] The second processor 3751 can be any conventional processor, such as a commercially available CPU. The processor may also include, for example, a Graphics Processing Unit (GPU), a Field Programmable Gate Array (FPGA), a System on Chip (SOC), an Application Specific Integrated Circuit (ASIC), or a combination thereof.

[0151] The second memory 3752 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0152] In addition to instruction 3753, the second memory 3752 can also store data, such as road maps, route information, vehicle position, direction, speed, and other data. The data stored in the second memory 3752 can be used by the computing platform 3750.

[0153] In this embodiment of the disclosure, the second processor 3751 may execute instructions 3753 to complete all or part of the steps of the warehouse location detection method provided in any of the above embodiments.

[0154] In another exemplary embodiment, the vehicle includes or is connected to the electronic devices described in the above embodiments of this disclosure.

[0155] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the warehouse location detection method provided in any of the above embodiments when executed by the programmable device.

[0156] Figure 13 This is a block diagram illustrating an apparatus 1900 according to an exemplary embodiment. For example, apparatus 1900 may be provided as a server. (Refer to...) Figure 13 The apparatus 1900 includes a second processing component 1922, which further includes one or more processors, and memory resources represented by a third memory 1932 for storing instructions executable by the second processing component 1922, such as application programs. The application programs stored in the third memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the second processing component 1922 is configured to execute instructions to perform the warehouse location detection method provided in any of the above embodiments.

[0157] Device 1900 may also include a second power supply component 1926 configured to perform power management of device 1900, a wired or wireless network interface 1950 configured to connect device 1900 to a network, and a second input / output interface 1958. Device 1900 can operate on an operating system, such as Windows Server, stored in memory 1932. TM macOS X TM Unix TM Linux TM FreeBSD TM Or similar.

[0158] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of this disclosure. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0159] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for detecting storage locations, characterized in that, include: Acquire the target image; The target image is detected to obtain the storage location detection result, wherein the storage location detection result includes: two entrance corner points and two side angles, wherein the side angles represent the size of the angle formed by the line connecting the side of the storage location and the two entrance corner points; The location of the storage location is determined based on the storage location detection results; The step of detecting the target image to obtain the location detection result includes: The target image is detected using a storage location detection model, wherein the storage location detection model is trained in advance based on sample images and the annotation information of the sample images. The annotation information is used to characterize the positions of the two entrance corner points of the storage location in the sample image, and to characterize the side angles corresponding to the two sides of the storage location or the positions of the two sides in the sample image. The corner points at the rear end of the storage location are not visible in the sample image.

2. The storage location detection method according to claim 1, characterized in that, The storage location detection results also include: the direction of entry into the storage area; Determining the location of the storage location based on the storage location detection result includes: Determine whether to adjust the angles of the two sides; In response to determining that the two side angles should be adjusted, the two side angles are adjusted according to the storage direction; The location of the storage location is determined based on the two entrance corner points and the adjusted two side angles.

3. The storage location detection method according to claim 2, characterized in that, Determining whether to adjust the two side angles includes: Calculate the target angle corresponding to the first included angle between the straight lines containing each of the said side angles; In response to the target angle being greater than or equal to a preset angle threshold, it is determined that the angles of the two sides will be adjusted.

4. The storage location detection method according to claim 2, characterized in that, The adjustment of the two side angles according to the warehousing direction includes: Calculate the size of the second angle between each of the aforementioned side angles and the direction of entry into the warehouse; Adjust the larger of the two side angles corresponding to the second included angle based on the direction of entry into the warehouse.

5. The storage location detection method according to claim 4, characterized in that, The step of adjusting the larger of the two side angles corresponding to the second included angle based on the warehousing direction includes: Adjust the larger of the two side angles to make the side corresponding to that side angle move closer to the storage direction.

6. A storage location detection device, characterized in that, include: The acquisition module is configured to acquire the target image; The detection module is configured to detect the target image to obtain the storage location detection result, wherein the storage location detection result includes: two entrance corner points and two side angles, wherein the side angles represent the size of the angle formed by the line connecting the side of the storage location and the two entrance corner points; The determination module is configured to determine the location of the storage location based on the storage location detection result; The detection module is configured to detect the target image using a storage location detection model. The storage location detection model is trained in advance based on sample images and annotation information. The annotation information is used to characterize the positions of the two entrance corners of the storage location in the sample image, and to characterize the side angles corresponding to the two sides of the storage location or the positions of the two sides in the sample image. The corners at the rear end of the storage location are not visible in the sample image.

7. An electronic device, characterized in that, include: First processor; A first memory for storing instructions executable by a first processor; The first processor is configured to run the executable instructions to implement the steps of the warehouse location detection method according to any one of claims 1 to 5.

8. A vehicle, characterized in that, include: Second processor; A second memory used to store instructions executable by a second processor; The second processor is configured to run the executable instructions to implement the steps of the warehouse location detection method according to any one of claims 1 to 5.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the program instructions are executed by the processor, they implement the steps of the storage location detection method as described in any one of claims 1 to 5.

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