Aiming at the technical problems of intelligent recognition and accurate positioning of steel rolling surface defects, a target detection method based on machine vision and depth neural network was proposed. YOLOX_M was introduced as the model of surface defect detection using the weights trained on the COCO dataset as the initial weights. To realize the identification and location of surface defect categories of rolled steel, the YOLOX_M model was further trained using the practical dataset. The performance of YOLOX_M was compared with the other five YOLOX models. The test results show that YOLOX_M can effectively detect six different forms of surface defects, and the test accuracy (P), recall rate (R) and detection mAP can respectively reach 88.81%, 80.88% and 90.12%. The mAP of the YOLOX_M model is higher than 90% and the model size is less than 100 MB, so it can be better applied in the embedded system for real-time detection.
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