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The one-stage YOLOv5 steel surface defect detection has issues such as slow operation speed, loss of defect location and semantic information of small targets, and inadequate extraction of defect features. This study proposed a defect detection algorithm with improved YOLOv5 to solve these issues. The proposed algorithm used the slim-neck layer built by three new modules instead of the neck layer in YOLOv5s to achieve a lightweight network model. In addition, the spatial perception self-attention mechanism was introduced to enhance the feature extraction capability of the initial convolutional layer without limiting the input size. The improved Atrous Spatial Pyramid Pooling was added to expand the perceptual field and capture multiscale contextual information while preventing local information loss and enhancing the relevance of long-range information. The experimental results showed that the improved YOLOv5 algorithm has a reduced model volume, significantly higher detection accuracy and speed than the traditional algorithm, and the ability to detect steel surface defects quickly and accurately.

Usage of an improved YOLOv5 for steel surface defect detection