Paper Info
Title
Quality Inspection Based on Quadrangular Object Detection for Deep Aperture Component
Abstract
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.
Year
DOI
Venue
2021
10.1109/TSMC.2019.2956776
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Keywords
DocType
Volume
Convolutional neural network,object detection,quality inspection,spring-wire socket
Journal
51
Issue
ISSN
Citations 
10
2168-2216
0
PageRank 
References 
Authors
0.34
9
6
Name
Order
Citations
PageRank
Jiabin Zhang151.43
Zheng-Tao Zhang2578.00
Hu Su341.75
Wei Zou4183.05
Xin-Yi Gong542.09
Feng Zhang662.28