An image processing apparatus includes an acquisition unit configured to acquire a read image obtained by reading a printed document, and a determination unit configured to determine presence or absence of a streak included in the read image, by tracking a contour of the printed document from each of a plurality of points that are both end points of the read image in a direction orthogonal to a reading direction of the read image.

A method for detecting defects in images, is employed in a computer device, and stored in a storage medium. The method trains an autoencoder model using unblemished images, inputting an image to be detected into the autoencoder model, and obtaining a reconstructed image. An image error is calculated between the image to be detected and the reconstructed image, and the image error is inputted into a student's t-distribution and a calculation result is obtained. In response that the calculation result falls within a preset defect determination criterion range, the image to be detected is determined to be an unblemished image. In response that the calculation result does not fall within the preset defect determination criterion range, the image to be detected is determined to be a defective image. The method improves the efficiency and accuracy of defect detection.

A part inspection system includes a vision device configured to image a part being inspected and generate a digital image of the part. The inspection system includes a part inspection module communicatively coupled to the vision device and receiving the digital image of the part. The part inspection module includes an image quality module. The image quality module analyzes the digital image to determine if the digital image achieves a quality threshold. The image quality module generates an image quality output based on the analysis of the digital image. The part inspection module includes an image classifier module. The image classifier module analyzes the digital image to classify the image as a defective part or a non-defective part.

One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed based upon data indicative of at least one of vehicle damage or a vehicle defect being shown in the one or more sets of imaging data. Based upon the analyzing of the one or more sets of imaging data, damage to the vehicle or a defect of the vehicle may be identified. The identified damage or defect may be compared to a claimed damage or defect to determine whether the claimed damage or defect occurred.

A method of determining a support structure depth of a support structure having a front and a back separated by the support structure depth includes: obtaining a point cloud of the support structure, and a mask indicating, for a plurality of portions of an image of the support structure captured from a capture pose, respective confidence levels that the portions depict the back of the support structure; selecting, from the point cloud, an initial set of points located within a field of view originating at the capture pose; selecting, from the initial set of points, an unoccluded subset of depth measurements, the depth measurements in the unoccluded subset corresponding to respective image coordinates; retrieving, from the mask, a confidence level for each of the depth measurements in the unoccluded subset; and based on the depth measurements in the unoccluded subset and the retrieved confidence levels, determining the support structure depth.

A set of images of a three-dimensional (3D) inspection object collected by a drone during execution of a first flight path may be received, along with telemetry data from the drone. A tagged set of images may be stored, with each tagged image being stored together with a corresponding drone position at a corresponding time that the tagged image was captured, as obtained from the telemetry data. A mapping of the set of tagged images to corresponding portions of a 3D model of the 3D inspection object may be executed, based on the telemetry data. Based on the mapping, at least one portion of the 3D inspection object omitted from the set of tagged images may be identified. A second flight path may be generated for the drone that specifies a position of the drone to capture an image of the at least one omitted portion of the 3D inspection object.

An inspection system for a vehicle underbody in an in-line process includes: a vehicle recognition unit for acquiring a vehicle ID by recognizing a vehicle entering an inspection process; a vision system that photographs the vehicle underbody through a plurality of cameras disposed under a vehicle moving direction (Y-axis) and disposed at vertical and diagonal angles along a width direction (X-axis) of the vehicle; and an inspection server that detects assembly defects of a component by performing at least one of a first vision inspection that matches an object image for each component through a rule-based algorithm or a secondary deep-learning inspection through a deep-learning engine by acquiring a vehicle underbody image photographed by operating the vision system with setting information suitable for a vehicle type and a specification according to the vehicle ID.

A checking mechanism for workshop security applying a method for workshop security comprises a transmission device, a recording device, a storage subassembly and a control subassembly. The transmission device comprises a first end (115) and a second end (116). The recording device records personnel and objects in their possession through a security check process, to establish a correspondence. The storage subassembly stores the information comprising personnel and their objects. The control subassembly is used for controlling the recording device to record and store the information to the storage subassembly. The control subassembly can retrieve the information recorded by the recording device to detect a certain person’s correspondence to the certain object.

The present invention provides a processing apparatus (10) including an acquisition unit (11) acquiring a captured image including a managed object related to a store, a foreign object region detection unit (12) detecting a foreign object region being a region in which a foreign object exists in the managed object included in the captured image, and a warning unit (13) executing warning processing depending on the size of the foreign object region.