7-days of FREE Audio papers, translation & more with Prime
7-days of FREE Prime access
7-days of FREE Audio papers, translation & more with Prime
7-days of FREE Prime access
https://doi.org/10.1109/tim.2021.3067221
Copy DOIPublication Date: Jan 1, 2021 | |
Citations: 26 |
The existing object detection algorithms based on the convolutional neural network (CNN) are always devoted to the detection of natural objects and have achieved admirable detection effects. At present, these detection algorithms have been applied to the detection of defect data. In fact, the detection of defect data is different from the detection of general natural object data. First, it has a large number of images without annotations (that is, normal images), and they each contain different background information. Second, its processing principles are fundamentally different from general object detection problems. Therefore, the application of a general object detection algorithm based on CNN may not be perfect in this problem. In this article, a novel defect detection network (DefectNet) is proposed to solve the problem of defect detection. It first uses a shared weight binary classification network to determine whether an image contains the targets and then uses the detection network to detect the targets. Theoretical deduction and experimental results fully confirm that it can effectively improve the detection speed and effect of the general object detection network based on CNN. (Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/li-phone/DefectNet.git</uri> )
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.