Research on Steel Surface Defect Identification based on YOLO

The number of surface defects in steel has always been a key measure of the quality of steel production. Traditional detection methods, such as the strobe method, have the problems of slow detection reaction and low accuracy. The object detection technology based on deep learning can effectively improve the detection performance by virtue of its strong real-time performance and high accuracy. However, in the actual production process, complex production environments and other industrial factors may affect the efficiency of object detection technology. We used an improved You Only Look Once (YOLO) framework to identify defects on the steel surface. This improved version of the YOLO model optimizes the original architecture to enhance the detection of small size and low contrast defects. As a real-time object detection system, YOLO is able to predict the locations and category probability of defects directly from the input image by integrating convolutional neural networks. In addition, we have adjusted the loss function and anchor frame strategy of the model to improve the detection accuracy and response speed in complex environments. The performance of the model in different types of defect identification was analyzed, and the applicability and efficiency of the model in the actual steel defect dataset NEU-DET were discussed.