Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Shi, Lukuia; * | Zu, Haorana | Tai, Jikaia | Niu, Weifeib
Affiliations: [a] School of Artificial Intelligence, Hebei University of Technology, Tianjin, China | [b] Tianjin Special Equipment Inspection Institute, Tianjin, China
Correspondence: [*] Corresponding author. Lukui Shi, School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China. E-mail: shilukui@scse.hebut.edu.cn.
Abstract: Because X-ray welding images have complex backgrounds and welding defects have different sizes and shapes, effectively detecting welding defects in X-ray images is still a challenge. To solve these problems, a shape-aware network (SA-NET) was proposed, whose core was the shape-aware module (SAM). SAM includes a free-shape region proposal network (FS-RPN) and a two-level regression head (TR-Head). FS-RPN predicts the shape of the anchor boxes corresponding to each position on the feature maps, and aligns the feature maps according to the predicted anchor box shape. Then, the offset and the foreground classification score of the anchor boxes are predicted according to the aligned feature maps. Thus, FS-RPN generates the proposal regions with a higher quality. TR-Head uses the first-level detection head, which only contains one regression branch, to further improve the quality of the proposal regions by fine-tuning the proposal regions. It employs the second-level detection head, which consists of one classification branch and one regression branch, to predict the categories and the boxes of defects. The experimental results showed that SA-NET effectively improved the quality of the proposal regions and greatly improved the detection effect of welding defects, especially defects with special shapes.
Keywords: Welding defects, defect detection, shape awareness, FS-RPN, TR-head
DOI: 10.3233/JIFS-220132
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6147-6162, 2022
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl