A Nested U-Shaped Residual Codec Network for Strip Steel Defect Detection

• Published in: Applied sciences 2022-12, Vol.12 (23), p.11967
• Main Authors: Guo, Huaping, Zhan, Shanggui, Zhang, Li, Zhu, Wenbo, Sun, Yange, Wang, Jing
• Format: Article
• Language: English
• Subjects: attention mechanisms; Background noise; Codec; Computer applications; Deep learning; Economic impact; encoder–decoder; Flaw detection; Mapping; Neural networks; Raw materials; Regularization methods; salient object detect; Semantics; Shipbuilding; Steel; surface defect; Wavelet transforms
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app122311967

Strip steel is an important raw material for the related industries, such as aerospace, shipbuilding, and pipelines, and any quality defects in the strip steel would lead to huge economic losses. However, it is still a challenge task to effectively detect the defects from the background of the strip steel due to its complex variations, including variable flaws, chaotic background, and noise invasion. This paper proposes a novel strip steel defect detection method based on a U-shaped residual network, including an encoder and a decoder. The encoder is a fully convolutional neural network in which attention mechanisms are embedded to adequately extract multi-scale defect features and ro ignore irrelevant background regions. The decoder is a U-shaped residual network to capture more contextual data from different scales, without significantly increasing the computational cost due to the pooling operations used in the U-shaped network. Furthermore, a residual refinement module is designed immediately after the decoder to further optimize the coarse defect map. Experimental results show that the proposed method can effectively segment surface defect objects from irrelevant background noise and is superior to other advanced methods with clear boundaries.