Research on multi-scale steel surface defects identification based on YOLO model

The number of surface defects in steel has consistently been a pivotal criterion for evaluation in the context of steel production. Conventional detection techniques, such as the strobe method, are deficient in several respects, including a slow response time and a lack of precision. Nevertheless, object detection technology based on deep learning can effectively address these issues due to its robust real-time performance and high accuracy. The proposed method capitalizes on the rapid and efficient characteristics of the YOLO model, integrating a multi-scale feature extraction module to fuse feature maps of varying scales, thereby enhancing the detection capabilities for a spectrum of steel surface defects. Specifically, the convolutional neural network layer in the YOLO model is employed to extract multi-scale features in the image in a stepwise manner. These features are then integrated together through a feature fusion strategy, thereby facilitating the accurate identification and location of steel surface defects. Experimental analysis demonstrates that the proposed method is markedly superior to the traditional detection method in terms of detection speed and accuracy, and can effectively enhance the performance of steel surface defect identification.