Implementation of surface crack detection method for nuclear fuel pellets guided by convolution neural network. Issue 7 (3rd July 2021)

• Record Type: Journal Article
• Authors: Zhang, Bin; Miao, Yanjie; Tian, Yongzhi; Zhang, Wenjie; Wu, Ge; Wang, Xinmiao; Zhang, Chaoying
• Is Part Of: Journal of nuclear science and technology. Volume 58:Issue 7(2021)
• Page Start: 787
• Page End: 796
• Publication Date: 2021-07-03
• Subjects: Nuclear fuel pellets -- crack detection -- convolutional neural network -- WOV threshold method -- image processing -- machine vision; Nuclear engineering -- Periodicals; Nuclear physics -- Periodicals; Nuclear energy -- Periodicals; 621.4805
• DOI: 10.1080/00223131.2020.1869622
• Languages: English
• ISSNs: 0022-3131

Abstract

ABSTRACT: Crack detection is one of the important contents of the nuclear fuel pellet quality inspection. Aiming at the problem of high crack false detection rate caused by low image contrast of nuclear fuel pellets, complex image background and fine cracks on the pellet surface, a Weighted Object Variance (WOV) threshold crack detection method guided by SE-CrackNet convolutional neural network is proposed. The method first uses the sliding window scanning technology and SE-CrackNet network to locate the crack regions in the pellet image, and then uses the WOV threshold method to extract the cracks to achieve accurate identification of the cracks on the surface of the nuclear fuel pellet. The pixel-level F1-measure of the method is about 92%, which can accurately identify cracks on the surface of nuclear fuel pellets, greatly reduce the crack false detection rate, meet the real-time quality inspection requirements of nuclear fuel pellet production lines, and vastly improve the performance of traditional machine vision inspection systems. At the same time, the method can be extended to the quality inspection of other industrial products.