Abstract
In order to reduce the influence of surface defects on the performance and appearance of hot-rolled steel strip, a surface defect detection method combining attention mechanism and multi-feature fusion network was proposed. In this method, the traditional SSD model was used as the basic framework, and the ResNet50 network after knowledge distillation was selected as the feature extraction network. The low-level features and high-level features were fused and complementary to improve the accuracy of detection. In addition, channel attention mechanism was introduced to filter and retain important information, which reduced the network computation and improves the network detection speed. The experimental results showed that the accuracy of RAF-SSD model for surface defect detection of hot rolled steel strip was significantly higher than that of traditional deep learning models, and the detection speed was 12.9% higher than that of SSD model, which can meet the real-time requirements of industrial detection.
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