Computers and electrical engineering have made great strides in steel plate manufacturing. defect recognition techniques have also evolved. However, due to the large scale of defects, diverse features and sample imbal...
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Computers and electrical engineering have made great strides in steel plate manufacturing. defect recognition techniques have also evolved. However, due to the large scale of defects, diverse features and sample imbalance problems of steel plates, the general algorithms often suffer from low recognition accuracy and weak robustness in practical detection. Aiming at the problems in recognition, this study proposes an improved defect segmentation network with coder-decoder structure to realize multi-scale interaction of features. Using the split-attention feature extraction module, defect features are learned adaptively. Meanwhile, combined with the group normalization module, a surface defect region recognition model based on depth feature fusion is established. The model was trained for comparative ablation using a migration learning approach. The experimental results confirm the efficiency of the technique. 89.11 % IoU and 94.24 % Dice can be achieved on the Severstal dataset using this method. The research can be applied as an intelligent system for quality monitoring throughout the production process, guiding its rational decision-making and control to realize the improvement of strip steel product quality.
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