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This article focuses on automatic inspection for the commonly used component called spring-wire socket. An automatic inspection system is built that adopts an endoscope to improve the imaging quality. To detect the low contrast targets in complex background, we adopt the pipeline of Faster R-CNN but with several improvements. The improved network specifies the targets in the form of quadrangular bounding box as opposed to previous methods that specify them by rectangular bounding box. With the quadrangular representation, additional shape and pose information is provided and unexpected overlaps and background disturbance could be avoided. In the network, an eight-dimensional (8-D) vector is designed to represent the quadrangular bounding box followed by the improved anchor mechanism in the region proposal network. And also, a novel overlap score calculation method is proposed. On the basis of the detection result, rules arisen from expertise are provided to determine the quality of the component in which way automatic quality inspection could be accomplished. The superiority of our detection network over existing ones is sufficiently demonstrated with the detection result of irregular targets in images captured in the industrial scenario. Meanwhile, successful inspection result proves that the system meets the industrial requirements in terms of both accuracy and speed and thus is of practical significance to industrial applications. [ABSTRACT FROM AUTHOR]

This paper investigates automatic quality inspection for the components with a small diameter and deep aperture. An automatic pick-and-place system is constructed, which employs an endoscope to achieve better image quality aiming at the characteristics of the component. A coarse-to-fine contour extraction algorithm with four steps is presented to inspect the component’s quality. First, approximate locations of the targets are estimated using faster region-based convolutional neural networks (faster RCNN). Second, the corresponding edge image is obtained by using the multiscale probability boundary (mPb) detector. Third, edge enhancement is performed, which is based on the Brownian motion model. Fourth, the corresponding contours are finely extracted by edge grouping. A shape analyzing algorithm is utilized to classify the components based on the extracted contours. Comparison experiments fully demonstrate the superiority of the proposed inspection method over existing methods. Meanwhile, successful inspection results on challenging real-world image data prove that the system is of practical significance to industrial applications. [ABSTRACT FROM AUTHOR]

As a critical electrical connector component in the modern industrial environment, spring-wire sockets and their manufacture quality are closely relevant to equipment safety. These types of defects in a component are difficult to properly distinguish due to the defect similarity and diversity. In such cases, defect types can only be determined using cumbersome human visual inspection. To satisfy the requirements of quality control, a machine vision apparatus for component inspection is presented in this paper. With a brief description of the apparatus system design, our emphasis is put on the defect recognition algorithm. A multitask convolutional neural network (CNN) is proposed for detecting those ambiguous defects. Compared with the image processing method in machine vision, the defect inspection problem is converted into object detection and classification problems. Instead of breaking it down into two separate tasks, we jointly handle both aspects in a single CNN. In addition, data augmentation methods are discussed to analyze their effects on defects recognition. Successful inspection results using the presented model are obtained using challenging real-world defect image data gathered from a spring-wire socket module inspection line in an industrial plant. [ABSTRACT FROM AUTHOR]

• A novel Category-aware Conv-Pooling module is proposed, which explores weak image tag annotation to extract spatial information. • Knowledge distillation strategy is adopted to force the feature of a student CADN to mimic that of a teacher CADN, leading to accuracy improvement. • As verified, weakly supervised defect detection is achieved and competitive results are obtained by using the proposed CADN method. • In CADN, human labeling effort, accuracy and speed are simultaneously considered, making the method practical in industrial applications. Large-scale data with human annotations is of crucial importance for training deep convolutional neural network (DCNN) to ensure stable and reliable performance. However, accurate annotations, such as bounding box and pixel-level annotations, demand expensive labeling efforts, which has prevented wide application of DCNN in industries. Focusing on the problem of surface defect detection, this paper proposes a weakly supervised learning method named Category-Aware object Detection network (CADN) to tackle the dilemma. CADN is trained with image tag annotations only and performs image classification and defect localization simultaneously. The weakly supervised learning is achieved by extracting category-aware spatial information in a classification pipeline. CADN could be equipped with either a lighter or a larger backbone network as the feature extractor resulting in better real-time performance or higher accuracy. To address the two conflicting objectives simultaneously, both of which are significant concerns in industrial applications, knowledge distillation strategy is adopted to force the learned features of a lighter CADN to mimic that of a larger CADN. Accordingly, the accuracy of the lighter CADN is improved while high real-time performance is maintained. The proposed approach is verified on our own defect dataset as well as on an open-source defect dataset. As demonstrated, satisfied performance is achieved by the proposed method, which could meet industrial requirements completely. Meanwhile, the method minimizes human efforts involved in image labelling, thus promoting the applications of DCNN in industries. [ABSTRACT FROM AUTHOR]