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

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.

Automated summary

Strip steel is a crucial material for industries like aerospace, shipbuilding, and pipelines, and any defects in the strip steel can lead to significant economic losses. Detecting these defects is challenging due to the complex variations in strip steel. This paper proposes a novel method based on a U-shaped residual network, utilizing attention mechanisms in the encoder to extract multi-scale defect features and a decoder to capture contextual data. Experimental results demonstrate that this method effectively segments surface defect objects with clear boundaries compared to other advanced techniques.