Method for verifying cable wire insertion and housing inspection system

Abstract

To provide a method for verifying cable wire insertion into a connector housing, and a housing inspection system.SOLUTION: A method for verifying cable wire insertion includes: receiving, from a camera system, an inspection image of a connector housing held in a housing retainer of a housing inspection system; via a connector recognition machine vision system, classifying the connector housing as a recognized connector housing type based, at least in part, on a comparison between the inspection image of the connector housing and a template image corresponding to the recognized connector housing type; and, for one or more cable cavities of the connector housing, automatically verifying whether a correct cable wire is inserted into the cable cavity according to the recognized connector housing type.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001]

[0001] The present invention relates generally to computer vision systems, and more particularly to automatically verifying cable wire insertion into connector housings. [Background technology]

[0002]

[0002] Various types of cable connectors are often used to conductively couple one cable to another cable and / or one cable to an electronic device for the transmission of data and / or power. In some embodiments, such connectors include one or more cable wires that are inserted into corresponding cable cavities in a connector housing. The size and shape of the connector housing, as well as the number and distribution of cable cavities contained within the connector housing, can vary from scenario to scenario depending on the purpose of the cable connector. Summary of the Invention

[0003] This summary is not an extensive overview of the specification. It is not intended to identify key or critical elements of the specification or to delineate any scope of particular embodiments or any claims of the specification. Its sole purpose is to present some concepts of the specification in a simplified form as a prelude to the more detailed description that is presented in the present disclosure.

[0004] A cable wire insertion verification method includes receiving, from a camera system, an inspection image of a connector housing held within a housing retainer of a housing inspection system. Via a connector recognition machine vision system, the connector housing is classified as a recognized connector housing type based at least in part on a comparison between the inspection image of the connector housing and a template image corresponding to the recognized connector housing type. For one or more cable cavities of the connector housing, the method includes automatically verifying whether the correct cable wires are inserted into the cable cavity according to the recognized connector housing type.

[0005]

[0005] The features, functions, and advantages discussed may be realized individually in various embodiments or may be combined in yet other embodiments, further details of which can be understood by reference to the following description and drawings. [Brief explanation of the drawings]

[0006] [Figure 1]

[0006] An exemplary cable connector is shown schematically including a cable housing into which multiple cable wires are inserted. [Figure 2]

[0007] 1 illustrates an exemplary cable wire insertion verification method. [Figure 3A]

[0008] 1A and 1B schematically illustrate an exemplary connector housing held within a housing retainer of a housing inspection system. [Figure 3B]

[0009] 10A and 10B illustrate the use of a camera system of a housing inspection system to capture inspection images of a connector housing. [Figure 3C]

[0010] 1A and 1B illustrate schematic diagrams of exemplary inspection images captured by a camera system. [Figure 4]

[0011] 10A and 10B schematically illustrate recognizing a recognized connector housing type based on an inspection image. [Figure 5]

[0012] 10 illustrates a schematic diagram of identifying correspondences between image features in a test image and a template image. [Figure 6]

[0013] 10A and 10B show schematic diagrams of cable wire insertion confirmation; [Figure 7]

[0014] 1 illustrates schematically steps in an exemplary insertion sequence. [Figure 8]

[0015] 1 illustrates a schematic diagram of an exemplary computing system. DETAILED DESCRIPTION OF THE INVENTION

[0007]

[0016] Construction of cable connectors typically involves one or more steps in which individual cable wires are inserted into cable cavities in the connector housing. Such insertion may be performed manually, such as by a human operator, and / or automatically, such as via a suitable machine or robotic insertion system. In either case, however, cable wire insertion can be inconsistent and prone to error. Furthermore, manual inspection of connector housings during manufacturing can be tedious and time-consuming, and may not always detect cable insertion errors.

[0008]

[0017] Accordingly, the present disclosure is directed to techniques for automatic cable wire insertion verification. Techniques described herein include capturing an inspection image of a connector housing held within a housing retainer of a housing inspection system. This may be done to classify the connector housing as a recognized connector housing type. For example, different connector housings may have cable cavities of different shapes, sizes, distributions, etc. Thus, by first recognizing the type of connector housing being inspected, the system can verify that the correct type of connector housing has been inserted into the housing inspection system and then evaluate whether the cable wires are being inserted into the correct cable cavities during a subsequent cable insertion sequence. In this manner, the housing inspection system may detect when an insertion error occurs, such as inserting a cable wire into the incorrect cable cavity and / or inserting the cable wire an incorrect distance. This may beneficially increase the speed and accuracy with which cable connectors are constructed and beneficially reduce the manual effort involved in such construction.

[0009]

[0018] The insertion of cable wires into a connector housing is illustrated generally with reference to FIG. 1 , which shows an exemplary cable connector 100. The cable connector includes a connector housing 102. The connector housing 102 includes multiple cable cavities. During assembly of the cable connector, multiple cables may be inserted into the multiple cable cavities. Some of the cable cavities are labeled in FIG. 1 as cable cavity 104. Additionally, FIG. 1 shows three different cable wires 106A, 106B, and 106C. Cable wires 106B and 106C have been inserted into their respective cable cavities in the connector housing, while cable wire 106A has not yet been inserted.

[0010]

[0019] It will be understood that the specific components shown in FIG. 1 , as well as those shown in FIGS. 2-8 described herein, are greatly simplified for purposes of explanation. The size, shape, and specific appearance of the components shown in FIGS. 1-8 are non-limiting and are not drawn to scale. Furthermore, it will be understood that the components shown in FIGS. 1-8 may be constructed from any suitable materials. For example, the connector housing, housing retainer, cable wires, cable contacts, and other components described herein may be constructed from any suitable combination of plastic and / or metal, as non-limiting examples.

[0011]

[0020] Although three different cable wires are shown in the example of Figure 1, it will be understood that any suitable number of different cable wires may be inserted into the connector housing. For example, the number of inserted cable wires may be equal to or less than the number of cable cavities in the connector housing. In other words, it will be understood that the specific configuration shown in Figure 1 is non-limiting, and that the techniques described herein may be applicable to cable connectors used to connect any suitable number of cable wires to each other and / or to an electronic device, such as a PCB.

[0012]

[0021] This disclosure focuses primarily on electrically conductive cables used to transmit power and / or data, however, in some embodiments, the cable connectors described herein may be used with cable wires that are not electrically conductive but include other suitable transmission media, such as fiber optic cables.

[0013]

[0022] As used herein, "cable wire" includes a length of material (e.g., copper wire, optical fiber) used for the transmission of data and / or power that is often covered with a protective material (e.g., plastic or rubber insulation, grounded shielding). In other words, the term "cable wire" often refers herein not just to the conductive (e.g., copper) or non-conductive (e.g., optical fiber) core of a cable, but may also refer to any coating, insulation, and / or shielding added to the core.

[0014]

[0023] A "cable" includes one or more different cable wires. If a cable includes only one cable wire, the terms "cable wire" and "cable" may be used interchangeably. However, in some examples, a cable includes two or more cable wires bundled together. For example, in some embodiments, a cable is a multi-conductor cable including two or more cable wires (e.g., different conductive copper wires, each covered with its own insulating cable jacket) and bundled together with additional insulation and / or shielding to form the multi-conductor cable. In some embodiments, a "cable" is a shielded twisted pair cable. In this case, different cable wires include pairs of twisted conductors protected by an insulating jacket. The twisted pairs are themselves bundled together and surrounded by additional shielding and / or insulation to form a shielded twisted pair cable. If a cable includes two or more different cable wires, the different cable wires can each be inserted into a different cable cavity of the connector housing.

[0015]

[0024] Generally, there is a correspondence between different specific cable wires and the cable cavities into which they are inserted. For example, different specific cable wires may have different purposes (e.g., to transmit power, to transmit data, to complete a ground connection) and therefore may be inserted into different specific cable cavities. A final connector may then be used to couple the cable wires to the correct downstream components (e.g., ground points, input / output lines, power inlets). In some cases, different cable wires have different, distinguishable appearances. For example, the cable wires may have different sizes (e.g., gauges), use different colors or types of insulating / protective jackets, use different materials for the cable wire cores (e.g., different conductive metals or non-conductive materials), and / or differ in any other suitable manner.

[0016]

[0025] In the embodiment of FIG. 1 , conductive cable contacts 108 are attached to the ends of cable wires 106A. However, in general, the ends of cable wires may be treated in any suitable manner. For example, in some embodiments, conductive contacts may be attached to the cable wires. If so, such contacts may have any suitable size and shape. In some cases, different types of conductive contacts may be attached to different cable wires inserted into the same connector housing. In some embodiments, the cable wires need not include conductive contacts. Rather, for example, the cable wires may terminate at exposed lengths of cable wire cores or in any other suitable manner.

[0017]

[0026] Each cable cavity of the connector housing is sized and shaped for insertion of a cable wire. As shown, cable wires 106B and 106C are inserted into a respective cable cavity of the connector housing. The cable cavities have any suitable size based on the size of the cable intended for insertion into the cable cavity. In some embodiments, the same connector housing may include different cable cavity sizes intended for insertion of cable wires having different sizes (e.g., different wire gauges).

[0018]

[0027] In some cases, the cable cavity is sized to receive an insulating jacket surrounding a core of cable wire (e.g., copper wire or fiber optic material), so that a length of insulated cable is inserted into the connector housing. In other embodiments, the insulating jacket can be cut away, so that only the cable core is inserted into the connector housing.

[0019]

[0028] Any suitable length of cable wire may be inserted into the connector housing. Generally, the cable wire is inserted deep enough into the connector housing to allow transmission of data and / or power between the cable wire and any component, such as another cable wire and / or electronic device, that is coupled to the connector housing. Additionally or alternatively, the cable wire may be inserted deep enough so that a retention feature within the connector housing holds the cable wire in place.

[0020]

[0029] However, as discussed above, in some cases, such insertion may be prone to insertion errors. For example, the cable wire may be inserted into the incorrect cable cavity and / or inserted an incorrect distance. Manual inspection and verification of cable wire insertion may be tedious and time-consuming. Accordingly, FIG. 2 illustrates an exemplary method 200 for automatic cable wire insertion verification. Steps of method 200 may be initiated, terminated, and / or repeated at any appropriate time and in response to any appropriate condition. Method 200 is primarily described as being performed by a housing inspection system including a controller executing software instructions to implement a machine vision system for housing recognition and insertion verification. However, the steps of method 200 may be performed by any suitable computing system of one or more computing devices, and any computing device performing the steps of method 200 may have any suitable capabilities, hardware configuration, and form factor. In some embodiments, method 200 is performed by computing system 800, described below with reference to FIG. 8.

[0021]

[0030] At 202, method 200 includes receiving, from a camera system, an inspection image of a connector housing held within a housing retainer of a housing inspection system. An exemplary housing inspection system is shown generally with respect to FIG. 3A. Specifically, FIG. 3A includes a schematic representation of a housing inspection system 300 used to inspect a connector housing 302. The connector housing includes a plurality of cable cavities, some of which are labeled cable cavities 304. During a subsequent insertion sequence, one or more cable wires may be inserted into corresponding cable cavities in the connector housing, as will be described in more detail below.

[0022]

[0031] The connector housing is retained within housing retainer 306. In this embodiment, the housing retainer includes two clamps that grip the sides of the connector housing, thereby using friction to hold the connector housing in place. However, it will be appreciated that the housing retainer may take the form of any suitable mechanism or structure that holds the connector housing in place while providing automatic cable insertion verification. For example, the housing retainer may hold the connector housing using friction, suction, magnetic attraction, adhesion, and / or any other suitable force. In some embodiments, the housing retainer may be sized and shaped to accept a wide variety of different types of connector housings having different shapes and sizes without requiring significant rework or reconfiguration of the housing retainer.

[0023]

[0032] The connector housing may be inserted into the housing inspection system in any suitable manner. In some embodiments, personnel load the connector housing into the housing inspection system before inserting the cable wires into the connector housing, and then remove the connector housing from the inspection system once cable insertion is complete. In some embodiments, insertion and / or removal of the connector housing may be performed by a suitable automated system, such as a suitable automated machine or robot.

[0024]

[0033] Similarly, while the connector housing is held in place by the housing retainer, the cable wires may be inserted into the connector housing in any suitable manner. For example, the cable wires may be inserted manually by personnel. Additionally or alternatively, the cable wires may be inserted automatically by any suitable automated system.

[0025]

[0034] In the embodiment of FIG. 3A , housing inspection system 300 is communicatively coupled to controller 308. A “controller” takes the form of any suitable computer logic hardware configured to execute instructions encoded in software, firmware, and / or hardware to thereby control the operation of the housing insertion system. For example, as will be described in more detail below, the controller can be used to control the operation of a camera system and / or implement a machine vision system used for connector housing recognition and cable wire insertion verification. If an automated system is used for the insertion of connector housings into the housing inspection system and / or for the insertion of cable wires into the connector housings, such an automated system can be controlled by controller 308.

[0026]

[0035] In this example, the controller is shown separate from the housing inspection system. For example, the controller may be at least partially integrated into a structure physically separate from the housing inspection system and communicatively coupled to the housing inspection system via any suitable wired or wireless connection. However, it will be understood that in some examples, the controller may be "on-board" the housing inspection system and integrated into the same physical assembly as the housing inspection system. In some examples, the controller 308 performs one or more steps of the method 200. In some examples, the controller 308 is implemented as a computing system 800, described below with respect to FIG. 8.

[0027]

[0036] In some cases, before the cable wires are inserted into the connector housing, the housing inspection system receives a set of cable insertion parameters that include information about the cable wire insertion process. In the embodiment of FIG. 3A, the controller 308 receives the set of cable insertion parameters 310. Generally, these include any information related to the subsequent cable insertion process, in which the cable wires are inserted into the cable cavity of the connector housing.

[0028]

[0037] For example, in some cases, the cable insertion parameters specify the intended type of connector housing to be inserted into a housing inspection system in a current assembly process. Once the connector housing is inserted, the housing inspection system may perform connector housing recognition to verify that the connector housing is the correct type. Additionally or alternatively, the cable insertion parameters may specify a correct insertion sequence in which one or more cable wires are inserted into one or more cable cavities of the connector housing. This may include the order in which the cable cavities are filled (e.g., based on different identifiers assigned to the cable cavities), the order in which different cable wires are inserted (e.g., based on different identifiers assigned to the cable wires), the mapping of different specific cables to different cable cavities, and / or any other suitable information. It will be appreciated that the cable insertion parameters may include any suitable information regarding cable wire insertion into a connector housing.

[0029]

[0038] The cable insertion parameters may be provided in any suitable manner. In some examples, the cable insertion parameters are provided by a human user, such as an operator or supervisor of the housing inspection system. For example, the human user may specify the cable insertion parameters by providing input to an appropriate input mechanism, such as a computer mouse, keyboard, and / or touch-sensitive display interface. Additionally or alternatively, the cable insertion parameters may be retrieved from computer storage. For example, the cable insertion parameters may be stored within local data storage hardware of the housing inspection system, loaded from a removable storage device, and / or accessed over a computer network.

[0030]

[0039] In some examples, multiple aspects of the cable insertion parameters may be displayed for viewing. For example, the cable insertion parameters may include a set of instructions to a human user for inserting the cable wires in the correct sequence. In some examples, the displayed instructions are updated in real time, for example, to provide feedback to the user regarding whether the correct cable wires were inserted in the final insertion step.

[0031]

[0040] Figure 3B schematically provides a different view of the connector housing held by a housing retainer of a housing inspection system. In Figure 3B, portions of the housing inspection system have been omitted to provide a top-down view (e.g., looking along the Y-axis as labeled in Figures 3A and 3B) of the connector housing 302 held by the housing retainer 306 within the housing inspection system 300. From this perspective, it can be seen that the housing inspection system includes a camera system 312. The camera system is configured to capture an inspection image 314 of the connector housing, as will be described in more detail below.

[0032]

[0041] In the example of FIG. 3B , camera system 312 is a stereo camera system including first camera 316A and second camera 316B. Each of these cameras may capture its own respective inspection image of the connector housing. One or both of these inspection images may be used for connector housing recognition and cable wire insertion verification. However, in general, the camera system may include any suitable number and type of different cameras for capturing insertion images. For example, in some examples, the camera system may include only a single camera. In some examples, the camera system may include three or more cameras. In general, increasing the number of cameras may improve the accuracy of the connector recognition and insertion verification process, but increases the complexity and cost of the housing inspection system. In other words, the housing inspection system may include a camera system of one or more suitable cameras. In this case, each camera may be sensitive to any suitable wavelength of electromagnetic radiation and may have any suitable image capture capabilities, including resolution, frame rate, and / or field of view.

[0033]

[0042] In one example, the camera system includes one or more grayscale or RGB cameras that are sensitive to visible wavelengths of light and output grayscale or RGB images. In some examples, the camera system includes one or more depth cameras in addition to or instead of the visible light camera and / or other suitable cameras. The depth cameras are configured to output depth images, where pixels of the depth images encode detected distances between the image sensors of the depth cameras and physical objects in the surrounding environment. Any suitable depth-sensing technology (e.g., stereoscopic, structured light, or time-of-flight) may be used.

[0034]

[0043] In embodiments where both a visible light camera and a depth camera are used, they may in some cases be used together as an integrated camera module. As one non-limiting example, an Intel® RealSense™ camera system may be used, which includes both an RGB camera and a depth camera in known alignment and outputs both RGB image data and depth image data.

[0035]

[0044] In the embodiment of FIG. 3B , the housing inspection system further includes an illumination system 318. The illumination system is configured to emit illumination light toward the connector housing. This may help to provide relatively uniform lighting conditions while inspection images of the connector housing are captured. The illumination system may take any suitable form and use any suitable hardware components to generate the illumination light. The illumination light may have any suitable intensity and use electromagnetic radiation of any suitable wavelength.

[0036]

[0045] The illumination light may be emitted at any suitable time. For example, in some cases, the illumination light may be provided whenever the housing inspection system is powered on. In some cases, the illumination light may be selectively switched on and off. For example, the illumination light may be emitted only when the connector housing is held within the housing retainer, or may be emitted only immediately prior to capturing an inspection image (e.g., the illumination light may act as a camera flash).

[0037]

[0046] In the embodiment of FIG. 3B , the camera system is attached to the housing inspection system at a fixed position relative to the housing retainer. This can improve the consistency of the inspection images captured by the camera system. For example, because the position of the camera system is fixed relative to the housing retainer, different connector housings held within the housing retainer can be imaged from approximately the same distance for each inspection image. In some cases, the housing retainer and / or connector housing are designed so that the connector housing is positioned a known, fixed distance from the camera system while the connector housing is held within the housing retainer. For example, the housing retainer may be designed to fit within a notch or groove in the connector housing. In some cases, while the connector housing is being loaded into the housing retainer, the connector housing is inserted such that features or markers on the connector housing align with features or markers on the housing retainer, indicating that the connector housing is properly positioned relative to the camera system.

[0038]

[0047] As shown, in this embodiment, the connector housing has two distinct faces on either side of the connector housing (relative to the Z axis as labeled in FIGS. 3A and 3B). This includes an insertion face, where the cable wires are inserted, and an observation face, which faces the camera system. In FIG. 3B, connector housing 302 includes insertion face 320 and observation face 322. The cable cavity of the connector housing extends from the insertion face of the connector housing to the observation face of the connector housing. Furthermore, the inspection image captured by the camera system shows the observation face. In this manner, as the cable wires are inserted into the connector housing, the tips of the cable wires can be visible in the inspection image captured by the camera system. This can be used to automatically verify correct cable wire insertion, as will be described in more detail below.

[0039]

[0048] 3C includes a schematic representation of an exemplary inspection image 314 captured by camera system 312 from connector housing 302. As discussed above, inspection image 314 shows the view side of the connector housing, which is opposite from the insertion side through which cable wires are inserted. In this example, a cable cavity extends from the insertion side through the connector housing to the view side. Thus, any cable wires inserted into the connector housing are visible in the inspection image captured from the view side.

[0040]

[0049] It will be understood that the images described herein need not be visually rendered or displayed for viewing by a human user. One exemplary inspection image is shown in FIG. 3C, but this is for illustrative purposes only. Rather, in some embodiments, the images are captured and stored by the housing inspection system for processing as digital data structures that are not visually represented on a computer display or otherwise presented for viewing.

[0041]

[0050] 2, at 204, the method 200 includes using a connector recognition machine vision system to classify the connector housing as a recognized connector housing type, which may be done at least in part based on a comparison between an inspection image of the connector housing and a template image corresponding to the recognized connector housing type.

[0042]

[0051] This process is illustrated generally with respect to FIG. 4, which shows an exemplary controller 400. Similar to controller 308 of FIG. 3A, controller 400 may be implemented as any suitable computer logic device. In some examples, controller 400 is implemented as computing system 800, described below with respect to FIG. 8. In FIG. 4, controller 400 is communicatively coupled to camera system 402 of a housing inspection system. From camera system 402, controller 400 receives inspection image 404 showing a connector housing held in a housing retainer, such as inspection image 314 of FIG. 3C. The inspection image is input to connector recognition machine vision system 406. Connector recognition machine vision system 406 is configured to classify the connector housing in the inspection image as a recognized connector housing type.

[0043]

[0052] The connector recognition machine vision system may be implemented in any suitable manner. In some embodiments, the connector recognition machine vision system includes one or more suitable artificial intelligence (AI) and / or machine learning (ML) models configured to classify input images. As one non-limiting example, the connector recognition machine vision system may be trained with a plurality of different template images corresponding to a plurality of different connector housing types. This is shown schematically in FIG. 4 . In that case, the connector recognition machine vision system is trained with a plurality of different template images, including template images 408A-C. Based on the inspection image 404 acquired from the camera system 402 and the template images 408A-C, the connector recognition machine vision system 406 outputs a recognized connector housing type 410, e.g., classifying a connector housing shown in the inspection image as one of the connector housing types represented in the template image.

[0044]

[0053] Any suitable ML and / or AI techniques can be used to implement the connector recognition machine vision system. In some cases, the connector recognition machine vision system includes a support vector machine. This support vector machine can be used to generate different classification models corresponding to different recognized connector housing types. Additionally or alternatively, the connector recognition machine vision system can include an artificial neural network, for example, the connector recognition machine vision system can include one of a support vector machine or an artificial neural network. In cases where a stereo camera system is used to capture inspection images of connector housings (such as those shown in FIG. 3B ), the connector recognition machine vision system can, in some cases, include a first recognition model trained to classify inspection images output by a first camera of the stereo camera system and a second recognition model trained to classify inspection images output by a second camera of the stereo camera. Thus, in one non-limiting approach, training the connector recognition machine vision system can include placing a connector housing in a housing inspection system and then capturing one or more images (referred to as “template” images) for the connector housing. This can be repeated for each connector housing type. These template images can then be loaded into a database, where an image classification model is generated (e.g., using a support vector machine or artificial neural network) to generate a separate, unique image signature for each connector type. In some embodiments, a "template image" may include a three-dimensional model or scan in addition to or instead of a two-dimensional image of the connector housing.

[0045]

[0054] In either case, the connector recognition machine vision system is configured to output a recognized connector housing type based at least in part on the inspection image. In some embodiments, classifying the connector housing includes verifying that the correct type of connector housing was inserted into the housing inspection system. For example, in some cases, the “correct” type of connector housing is already known. For example, the “correct” type may be specified in the cable insertion parameters, as described above with respect to FIG. 3A . Thus, classifying the connector housing can help verify that the correct type of housing was inserted. In some cases, the housing inspection system may output an error if an incorrect connector housing type is detected. This can capture scenarios, for example, where a worker loads the wrong type of connector housing into the housing inspection system.

[0046]

[0055] Alternatively, in some embodiments, connector housings are classified substantially blindly, e.g., without prior knowledge of the "correct" connector housing type for the current type of cable connector being built. In one exemplary scenario, a user loads an appropriate connector housing into a housing inspection system. The housing inspection system can then classify the connector housing as a recognized connector housing type. From there, the system can automatically retrieve the correct insertion sequence and set of insertion instructions for the recognized connector housing type. The user can then insert the cable wires into the connector housing accordingly.

[0047]

[0056] The output of the connector recognition machine vision system may take any suitable form. As one non-limiting example, an inspection image may be provided to different classification models corresponding to different recognized connector housing types. The different classification models then each output a prediction score indicating the model's confidence that the inspection image corresponds to that model's connector housing type. These prediction scores may then be aggregated and compared to a classification threshold. If the most likely prediction score exceeds the classification threshold, the machine vision system classifies the connector housing as belonging to the connector housing type corresponding to the most likely prediction score. If none of the prediction scores exceed the classification threshold, the machine vision system may output a classification error. The classification threshold may have any suitable value depending on the implementation. For example, the value may be set by a user or operator to balance the risk of incorrect classification against the risk of classification error.

[0048]

[0057] Once the connector housing is classified, the housing inspection system may, in some cases, perform cavity detection to identify the location of cable cavities in the connector housing. In some examples, detecting portions of cable cavities may include detecting a correspondence between image features in the inspection image and image features in the template image. For example, once a template image for a particular connector housing type is captured, portions of cable cavities in the template image may be manually labeled by a human user and / or automatically detected via a suitable computer vision system (e.g., a suitable ML and / or AI model). Thus, by detecting a correspondence between the inspection image and the template image, portions of cable cavities in the inspection image may be detected.

[0049]

[0058] This is illustrated diagrammatically with respect to Figure 5, which includes an exemplary inspection image 500 showing a connector housing held in a housing inspection system. Inspection image 500 is compared to a template image 502 showing the same type of connector housing as the inspection image. In Figure 5, features 504A and 504B of inspection image 500 correspond to image features 506A and 506B of template image 502.

[0050]

[0059] Any suitable approach can be used to detect such image feature correspondences. In some cases, a suitable feature recognition algorithm, such as Scale-Invariant Feature Transform (SIFT) and / or Fast Library for Approximate Nearest Neighbors (FLANN), can be used to identify correspondences between image features in two different images. In one exemplary approach, SIFT can first be applied to both images to detect keypoints and compute their descriptors. SIFT keypoints are points in an image that are invariant to scale and rotation, and each keypoint has an associated descriptor. These descriptors effectively capture local gradient information around the keypoints, making them distinguishable and robust to changes in viewpoint and lighting.

[0051]

[0060] Once SIFT descriptors are obtained from both images, FLANN can be used to match corresponding descriptors between the two images. FLANN is an algorithm for efficiently finding approximate nearest neighbors in a high-dimensional space. It speeds up the search for the closest match of each descriptor in one image to a descriptor in another image. In this process, each descriptor in the first image is compared to all descriptors in the second image to find the best match. The FLANN algorithm efficiently searches for the nearest neighbors (i.e., the most similar descriptors) in this high-dimensional space. Typically, the nearest neighbor is identified as the best match, but a second nearest neighbor may also be considered to ensure uniqueness of the match and filter out false matches. The output of this process is a set of keypoint pairs. Each pair then consists of a keypoint from the first image and its corresponding keypoint in the second image. For example, these keypoints may represent matching cable cavities between the two images.

[0052]

[0061] In some embodiments, detecting correspondences between cable cavities in the inspection image and the template image includes generating a homography matrix to account for rotations of the positions of one or more cable cavities caused by rotations of the connector housing in the inspection image relative to the template image. This is shown schematically with respect to FIG. 5. In that case, homography matrix 508 is generated to account for rotations of the connector housing between the template image and the inspection image.

[0053]

[0062] In general, the homography matrix can be generated in any suitable manner. If SIFT is used, the matched keypoints will inherently account for the rotation of the connector housing between the two images because SIFT descriptors are rotation-invariant. That is, even if the connector housing is rotated in one image relative to the other, the SIFT algorithm will still be able to find the corresponding keypoints. The matched keypoints can be used to estimate a homography matrix that describes how points in one image transform to points in the other image under a planar perspective transformation. This includes translation, rotation, scaling, and perspective distortion. The goal is to solve for a homography matrix H that satisfies the equation p' = Hp, where p and p' are corresponding points in the two images. This can be done using methods such as direct linear regression (DLT) or, to robustly handle outliers, using algorithms such as random sample consensus (RANSAC). The result is a homography matrix that, when applied to points in one image, maps those points to their corresponding points in the other image. This matrix takes into account all planar transformations, including any rotations that occur between two views of the object.

[0054]

[0063] Briefly referring back to FIG. 2 , at 206, method 200 includes automatically verifying, for one or more cable cavities of a connector housing, whether the correct cable wires are inserted into the cable cavity according to the recognized connector housing type. Generally, a set of cable wires may be inserted into one or more cable cavities over a series of one or more cable insertion steps in an insertion sequence. After each insertion step and / or after completion of the entire insertion sequence, images captured by the camera sequence may be used to verify whether the cable wires are correctly inserted. For example, whether the correct cable wires are inserted into the correct corresponding cable cavities. In other words, in some embodiments, the housing inspection system may verify after each insertion step whether the last-inserted cable wire is inserted into the correct cable cavity of the one or more cable cavities. Additionally or alternatively, each cable wire may be verified once after completion of the entire insertion sequence.

[0055]

[0064] One exemplary approach to cable wire insertion verification is illustrated generally with respect to FIG. 6 . FIG. 6 illustrates an exemplary image 600 of a connector housing 601. The connector housing 601 includes multiple cable cavities. One of the multiple cable cavities is labeled cable cavity 602. In this example, all of the cable cavities in the connector housing 601 are currently empty, but this need not always be the case. Rather, the approach illustrated with respect to FIG. 6 can be performed any time the connector housing still includes at least one cable cavity that is empty. Image 600 is referred to as a “pre-insertion image” because image 600 is captured before the connector insertion step 604 of the cable insertion sequence.

[0056]

[0065] After connector insertion step 604, the camera system is again used to capture a post-insertion image 606 depicting the connector housing 601. However, in this case, the cable wire 608 has been inserted into the cable cavity 602. The cable wire 608 is partially visible in the post-insertion image 606. In this exemplary approach, both the pre-insertion image 600 and the post-insertion image 606 are input into an image subtraction process 610. The image subtraction process 610 subtracts pixel values ​​of the post-insertion image from pixel values ​​of the pre-insertion image (or vice versa). Because the only change between the two images was the insertion of the cable wire, many pixel values ​​in the image subtraction result 612 will be zero or near zero. Thus, the image subtraction result will have the effect of highlighting any changes in the appearance of the connector housing between the capture of the two images (e.g., pixels indicating the cable wire 608 inserted into the cable cavity 602). In other words, the housing inspection system generates the image subtraction result based on the pre-insertion image and the post-insertion image. The result of this image subtraction can be used to detect movement (eg, the insertion of a cable wire) between the two images.

[0057]

[0066] In the approach of FIG. 6 , the image subtraction result 612 is provided to an insertion confirmation machine vision system 614. The insertion confirmation machine vision system 614 is trained to output a correct insertion probability 616 based on the image subtraction result. In some cases, upon detecting movement in the image subtraction result (e.g., a region of pixels having pixel values ​​higher than a motion threshold), a region of interest centered on the detected movement is input to the insertion confirmation machine vision system 614. If the correct insertion probability 616 exceeds the confirmation threshold, the housing inspection system may confirm that the correct cable wire has been inserted. If the correct insertion probability 616 does not exceed the confirmation threshold, the housing inspection system may output an error stating that an incorrect insertion may have occurred. For example, this may prompt work personnel to remove the last inserted cable wire and try again. It will be understood that the probability threshold may have any appropriate value depending on the implementation (e.g., to balance the risk of a false error notification and an undetected insertion error). In some cases, this process may be performed for each image captured by the camera system. For example, where the camera system is a stereo camera system that captures two different images at once, each of these images can then be used as the basis for performing cable wire insertion verification.

[0058]

[0067] The insertion confirmation machine vision system may take any suitable form and be trained in any suitable manner. In one exemplary approach, training the insertion confirmation machine vision system may include first capturing one or more images of an empty connector housing (e.g., an “empty image”). Next, additional images of the connector housing may be captured after the cable cavities of the connector housing have been filled with the correct corresponding cable wires (e.g., a “filled image”). These empty images and the filled reference image may then be used to train a classification algorithm. This classification algorithm may be used for inference to determine whether the cable wires are properly inserted into the connector housing. As with the connector recognition machine vision system, any suitable ML and / or AI techniques may be used. As one non-limiting example, the insertion confirmation machine learning system may use a support vector machine.

[0059]

[0068] In some cases, inserting the "correct" cable wire may include determining that the correct cable cavities were filled according to the insertion sequence. In other words, in some cases, the system does not distinguish between different specific cable wires, but rather assumes that the sequence is correctly followed if the cable wire is inserted into the next cable cavity in the insertion sequence. Alternatively, in some cases, different cable wires have sufficiently different appearances (e.g., size, color, contact type) that cable wire insertion confirmation may include determining that the correct individual cable wire was inserted into the correct corresponding cable cavity in the connector housing.

[0060]

[0069] In either case, the process shown in Figure 6 may be repeated any suitable number of times during the entire insertion sequence, where one or more different cable wires are inserted into the cable cavity of the connector housing. As noted above, in some cases, insertion verification occurs after each insertion step in the insertion sequence. In other embodiments, insertion verification may occur only after the entire insertion sequence is complete, e.g., after all cable wires have been inserted.

[0061]

[0070] FIG. 7 schematically illustrates one exemplary insertion sequence 700. As shown, in this embodiment, the insertion sequence includes multiple steps 702A and 702B, in which different cable wires are inserted into the connector housing. In FIG. 7, after each insertion step, the housing inspection system performs an insertion verification step 704A / 704B to determine whether the last insertion step was correct, e.g., whether the correct cable wires are inserted into the correct corresponding cable cavities. The insertion sequence 700 may continue with any appropriate number of subsequent steps, depending on the number of cable cavities in the connector housing and the number of cable wires to be inserted.

[0062]

[0071] The methods and processes described herein may be coupled to the computing system of one or more computing devices. In particular, such methods and processes may be implemented as an executable computer application program, a network-accessible computing service, an application programming interface (API), a library, or a combination of the above and / or other computing resources.

[0063]

[0072] 8 illustrates a simplified representation of an exemplary computing system 800 configured to provide any or all of the computing functionality described herein. Computing system 800 may take the form of one or more network-accessible devices, personal computers, server computers, portable computing devices, and / or other computing devices.

[0064]

[0073] Computing system 800 includes a logic subsystem 802 and a storage subsystem 804. Computing system 800 may optionally include a display subsystem 806, an input subsystem 808, a communication subsystem 810, and / or other subsystems not shown in FIG.

[0065]

[0074] The logic subsystem 802 includes one or more physical devices configured to execute instructions. For example, the logic subsystem may be configured to execute instructions. The instructions are part of one or more applications, services, programs, or other logical structures. The logic subsystem may include one or more hardware processors configured to execute software instructions. Additionally or alternatively, the logic subsystem may include one or more hardware or firmware devices configured to execute hardware or firmware instructions. The processors of the logic subsystem may be single-core or multi-core, and the instructions executed by the processors may be configured for sequential, parallel, and / or distributed processing. Individual components of the logic subsystem may optionally be distributed across two or more separate devices. These devices may be remotely located and / or configured for coordinated processing. Aspects of the logic subsystem may be virtualized and executed by remotely accessible networked computing devices configured as a cloud computing configuration.

[0066]

[0075] The storage subsystem 804 includes one or more physical devices configured to temporarily and / or permanently store computer information, such as data and instructions, executable by the logic subsystem. When the storage subsystem includes two or more devices, these devices may be co-located and / or remotely located. The storage subsystem 804 may include volatile devices, non-volatile devices, dynamic devices, static devices, read / write devices, read-only devices, random access devices, sequential access devices, location-addressable devices, file-addressable devices, and / or content-addressable devices. The storage subsystem 804 may include removable and / or internal devices. When the logic subsystem executes instructions, the state of the storage subsystem 804 may be transformed, for example, to hold different data.

[0067]

[0076] Logic subsystem 802 and storage subsystem 804 may be integrated into one or more hardware logic components, which may include program and application specific integrated circuits (PASICs / ASICs), program and application specific standard products (PSSPs / ASSPs), systems on a chip (SOCs), and complex programmable logic devices (CPLDs).

[0068]

[0077] The logic subsystem and storage subsystem may cooperate to instantiate one or more logic machines. As used herein, the term “machine” collectively refers to a combination of hardware, firmware, software, instructions, and / or any other components that cooperate to provide computer functionality. In other words, a “machine” is never an abstract idea but always has a concrete form. A machine may be instantiated by a single computing device, or a machine may include two or more subcomponents instantiated by two or more different computing devices. In some embodiments, a machine includes a local component (e.g., a software application executed by a computer processor) that cooperates with a remote component (e.g., a cloud computing service provided by a network of server computers). The software and / or other instructions that give a particular machine its functionality may optionally be stored as one or more unexecuted modules on one or more suitable storage devices.

[0069]

[0078] When included, the display subsystem 806 can be used to present a visual representation of the data maintained by the storage subsystem 804. This visual representation can take the form of a graphical user interface (GUI). The display subsystem 806 can include one or more display devices utilizing virtually any type of technology. In some implementations, the display subsystem can include one or more virtual, augmented, or mixed reality displays.

[0070]

[0079] When included, the input subsystem 808 may include or interact with one or more input devices. Input devices may include sensor devices or user input devices. Examples of user input devices include a keyboard, a mouse, a touchscreen, or a game controller. In some embodiments, the input subsystem may include or interact with selected natural user input (NUI) components. Such components may be integrated or peripheral, and input act transmission and / or processing may be handled on-board or off-board. Exemplary NUI components may include microphones for speech and / or voice recognition; infrared, color, stereo, and / or depth cameras for machine vision and / or gesture recognition; head trackers, eye trackers, accelerometers, and / or gyroscopes for motion detection and / or intent recognition.

[0071]

[0080] If included, communications subsystem 810 may be configured to communicatively couple computing system 800 with one or more other computing devices. Communications subsystem 810 may include wired and / or wireless communication equipment compatible with one or more different communications protocols. Communications subsystem may be configured for communication over personal, local, and / or wide area networks.

[0072]

[0081] The present disclosure is presented by way of example and with reference to the associated drawings. Components, process steps, and other elements that may be substantially the same in one or more of the drawings are identified collectively and described with minimal repetition. It should be noted, however, that collectively identified elements may also differ to some extent. It should be further noted that some of the drawings are schematic and not to scale. Various drawing scales, aspect ratios, and numbers of elements shown in the drawings may be intentionally distorted to more clearly show particular features or relationships.

[0073]

[0082] In one embodiment, a method for verifying cable wire insertion includes receiving, from a camera system, an inspection image of a connector housing held in a housing retainer of a housing inspection system; classifying, via a connector recognition machine vision system, the connector housing as the recognized connector housing type based at least in part on a comparison between the inspection image of the connector housing and a template image corresponding to the recognized connector housing type; and automatically verifying, for one or more cable cavities of the connector housing, whether the correct cable wires are inserted into the cable cavity according to the recognized connector housing type. In this or any other embodiment, the connector recognition machine vision system is trained with a plurality of different template images corresponding to a plurality of different connector housing types. In this or any other embodiment, the connector recognition machine vision system includes one of a support vector machine or an artificial neural network. In this or any other embodiment, the camera system is a stereo camera system including a first camera and a second camera, and the connector recognition machine vision system includes a first recognition model trained to classify inspection images output by the first camera and a second recognition model trained to classify inspection images output by the second camera. In this or any other embodiment, the camera system is attached to the housing inspection system at a fixed position relative to the housing retainer. In this or any other embodiment, the one or more cable cavities of the connector housing extend from an insertion face of the connector housing to an observation face of the connector housing, and the inspection image of the connector housing shows the observation face. In this or any other embodiment, the housing inspection system further includes an illumination system configured to emit illumination light toward the observation face of the connector housing.In this or any other embodiment, the method further includes detecting positions of the one or more cable cavities in the inspection image by detecting a correspondence between image features in the inspection image and image features in the template image. In this or any other embodiment, the method further includes generating a homography matrix to account for rotation of the positions of the one or more cable cavities caused by rotation of the connector housing relative to the template image. In this or any other embodiment, a set of cable wires is inserted into the one or more cable cavities over a series of one or more cable insertion steps in an insertion sequence, and after each insertion step, the method further includes automatically verifying whether the last-inserted cable wire is inserted into the correct cable cavity of the one or more cable cavities. In this or any other embodiment, the method further includes capturing a pre-insertion image before each insertion step, capturing a post-insertion image after each insertion step, and generating an image subtraction result based on the pre-insertion image and the post-insertion image. In this or any other embodiment, the image subtraction results are provided to an insertion confirmation machine vision system that is trained to output a probability of correct insertion based on the image subtraction results. In this or any other embodiment, the method further includes, prior to cable wire insertion, receiving cable insertion parameters that specify a correct insertion sequence for inserting one or more cable wires into the one or more cable cavities of the connector housing.

[0074]

[0083] In one embodiment, a housing inspection system includes a controller configured to receive, from a camera system, an inspection image of a connector housing held in a housing retainer of the housing inspection system; classify, via a connector recognition machine vision system, the connector housing as the recognized connector housing type based at least in part on a comparison between the inspection image of the connector housing and a template image corresponding to the recognized connector housing type; and automatically verify, for one or more cable cavities of the connector housing, whether the correct cable wires are inserted into the cable cavity according to the recognized connector housing type. In this or any other embodiment, the connector recognition machine vision system is trained with a plurality of different template images corresponding to a plurality of different connector housing types. In this or any other embodiment, the camera system is a stereo camera system including a first camera and a second camera, and the connector recognition machine vision system includes a first recognition model trained to classify inspection images output by the first camera and a second recognition model trained to classify inspection images output by the second camera. In this or any other embodiment, the controller is further configured to detect locations of the one or more cable cavities in the inspection image by detecting a correspondence between image features in the inspection image and image features in the template image. In this or any other embodiment, the controller is further configured to generate a homography matrix to account for rotation of the locations of the one or more cable cavities caused by rotation of the connector housing relative to the template image.In this or any other embodiment, a set of cable wires is inserted into the one or more cable cavities over a series of one or more cable insertion steps in an insertion sequence, and after each insertion step, the controller is further configured to automatically verify whether the last inserted cable wire is inserted into the correct cable cavity of the one or more cable cavities.

[0075]

[0084] In one embodiment, a cable wire insertion verification method includes receiving an inspection image of a connector housing held in a housing retainer of a housing inspection system from a camera system, the camera system being attached to the housing inspection system in a fixed position relative to the housing retainer; classifying the connector housing as the recognized connector housing type based at least in part on a comparison between the inspection image of the connector housing and the template image corresponding to the recognized connector housing type by identifying a correspondence between cable cavities detected in the inspection image and cable cavities in a template image via a connector recognition machine vision system; and automatically verifying whether the correct cable wires are inserted into the cable cavities of the connector housing according to the recognized connector housing type after each insertion step of a series of insertion steps in an insertion sequence.

[0076]

[0085] It will be understood that the configurations and / or approaches described herein are exemplary in nature, and that these specific embodiments or examples should not be considered limiting, as numerous variations are possible. The specific routines or methods described herein may represent one or more of any number of processing strategies. As such, various operations illustrated and / or described may be performed in the order illustrated and / or described, in other orders, concurrently, or omitted. Similarly, the order of processes described above may be changed.

[0077]

[0086] The subject matter of the present disclosure includes all novel and non-obvious combinations and subcombinations of the various processes, systems, and configurations, as well as other features, functions, operations, and / or properties disclosed herein, and any and all equivalents thereof. [Explanation of symbols]

[0078] 100 Cable Connector 102 Connector housing 104 Cable Cavity 106A~106C Cable Wire 108 Conductive cable contacts 200 ways 202~206 Method steps 300 Housing Inspection System 302 Connector Housing 304 Cable Cavity 306 Housing Retainer 308 Controller 310 Cable Insertion Parameters 312 Camera System 314 Inspection Images 316A First Camera 316B Second Camera 318 Lighting System 320 Insertion surface 322 Observation surface 300 Controller 402 Camera System 404 Inspection Images 406 Connector Recognition Machine Vision System 408A~408C Template images 410 Recognized Connector Housing Types 500 inspection images 502 template images 504A, 504B Inspection image characteristics 506A, 506B Template image features 508 Homography Matrix 600 Before Insertion Images 601 Connector Housing 602 Cable Cavity 604 Connector Insertion Step 606 Image after insertion 608 Cable Wire 610 Image Subtraction Process 612 Image subtraction results 614 Insertion confirmation machine vision system 616 Probability of correct insertion 700 Insertion Sequences 702A, 702B Insertion Step 704A, 704B Insertion confirmation step 800 Computing Systems 802 Logic Subsystem 804 Storage Subsystem 806 Display Subsystem 808 Input Subsystem 810 Communication Subsystem

Claims

1. A cable wire insertion verification method (200), comprising: receiving (202) an inspection image (404) of a connector housing (302) held within a housing retainer (306) of a housing inspection system (300) from a camera system (402); classifying (204) the connector housing (302) as the recognized connector housing type (410) based at least in part on a comparison between the inspection image (404) of the connector housing (302) and a template image (408) corresponding to the recognized connector housing type (410) via a connector recognition machine vision system (406); and The method (200) includes automatically verifying (206) whether the correct cable wires (108) are inserted into one or more cable cavities (304) of the connector housing (302) according to the recognized connector housing type (410).

2. 10. The method of claim 1, wherein the connector recognition machine vision system is trained with a plurality of different template images corresponding to a plurality of different connector housing types.

3. 3. The method of claim 2, wherein the connector recognition machine vision system comprises one of a support vector machine or an artificial neural network.

4. 2. The method of claim 1, wherein the camera system is a stereo camera system including a first camera and a second camera, and the connector recognition machine vision system includes a first recognition model trained to classify inspection images output by the first camera and a second recognition model trained to classify inspection images output by the second camera.

5. 2. The method (200) of claim 1, wherein the camera system (312) is mounted to the housing inspection system (300) in a fixed position relative to the housing retainer (306).

6. 2. The method of claim 1, wherein the one or more cable cavities of the connector housing extend from an insertion face of the connector housing to an observation face of the connector housing, and the inspection image of the connector housing shows the observation face.

7. 7. The method of claim 6, wherein the housing inspection system further comprises an illumination system configured to emit illumination light toward the viewing surface of the connector housing.

8. 2. The method (200) of claim 1, further comprising detecting the location of the one or more cable cavities (304) in the inspection image (314) by detecting a correspondence between image features (504) in the inspection image (314) and image features (506) in the template image (502).

9. 9. The method of claim 8, further comprising generating a homography matrix to account for rotation of the positions of the one or more cable cavities caused by rotation of the connector housing relative to the template image.

10. 2. The method (200) of claim 1, wherein a set of cable wires (108) are inserted into the one or more cable cavities (304) over a series of one or more cable insertion steps (702) of an insertion sequence (700), and after each insertion step (702), the method further comprises automatically verifying whether the last-inserted cable wire (108) is inserted into the correct cable cavity of the one or more cable cavities (304).

11. 11. The method (200) of claim 10, further comprising capturing a before-insertion image (600) before each insertion step (702), capturing a after-insertion image (606) after each insertion step (702), and generating an image subtraction result (612) based on the before-insertion image (600) and the after-insertion image (606).

12. 12. The method of claim 11, wherein the image subtraction results are provided to an insertion confirmation machine vision system that is trained to output a probability of correct insertion based on the image subtraction results.

13. 2. The method of claim 1, further comprising receiving cable insertion parameters that specify a correct insertion sequence for one or more cable wires to be inserted into the one or more cable cavities of the connector housing prior to cable wire insertion.

14. A housing inspection system (300) comprising a controller (308), the controller (308) comprising: receiving, from a camera system (402), an inspection image (404) of a connector housing (302) held within a housing retainer (306) of the housing inspection system (300); classifying, via a connector recognition machine vision system (406), the connector housing (302) as the recognized connector housing type (410) based at least in part on a comparison between the inspection image (404) of the connector housing (302) and a template image (408) corresponding to the recognized connector housing type (410); and The housing inspection system (300) is configured to automatically verify whether the correct cable wire (108) is inserted into one or more cable cavities (304) of the connector housing (302) according to the recognized connector housing type (410).

15. 15. The housing inspection system (300) of claim 14, wherein the connector recognition machine vision system (406) is trained with a plurality of different template images (408) corresponding to a plurality of different connector housing types.

16. 15. The housing inspection system (300) of claim 14, wherein the camera system (402) is a stereo camera system including a first camera (316A) and a second camera (316B), and the connector recognition machine vision system (406) includes a first recognition model trained to classify inspection images output by the first camera (316A) and a second recognition model trained to classify inspection images output by the second camera (316B).

17. 15. The housing inspection system (300) of claim 14, wherein the controller (308) is further configured to detect the location of the one or more cable cavities (304) in the inspection image (404) by detecting a correspondence between image features (504) in the inspection image (404) and image features (506) in the template image (502).

18. 18. The housing inspection system (300) of claim 17, wherein the controller (308) is further configured to generate a homography matrix (508) to account for rotation of the position of the one or more cable cavities (304) caused by rotation of the connector housing (302) relative to the template image (502).

19. 15. The housing inspection system (300) of claim 14, wherein a set of cable wires (108) is inserted into the one or more cable cavities (304) over a series of one or more cable insertion steps (702) of an insertion sequence (700), and after each insertion step (702), the controller (308) is further configured to automatically verify whether the last-inserted cable wire (108) is inserted into the correct cable cavity among the one or more cable cavities (304).

20. A cable wire insertion verification method (200), comprising: receiving an inspection image (404) of a connector housing (302) held within a housing retainer (306) of a housing inspection system (300) from a camera system (402), the camera system (402) being attached to the housing inspection system (300) in a fixed position relative to the housing retainer (306); classifying (204) the connector housing (302) as the recognized connector housing type (410) based at least in part on a comparison between the inspection image (404) of the connector housing (302) and the template image (408) corresponding to the recognized connector housing type (410) by identifying a correspondence between the cable cavities (304) detected in the inspection image (404) and the cable cavities (304) in a template image (408) via a connector recognition machine vision system (406); and The method (200) includes automatically verifying (206) whether the correct cable wire (108) is inserted into the cable cavity (304) of the connector housing (302) according to the recognized connector housing type (410) after each insertion step (702) of a series of insertion steps of an insertion sequence (700).

Images