Improvement of defect detection for steel based on YOLOv7 algorithm

Abstract Working on the conundrum of low efficiency of strip surface defect detection, a surface defect detection algorithm for boards and strips leveraging YOLOV7 is introduced. First, the global attention mechanism is set up to enhance global information interaction and expression capabilities, and improve detection performance; in second place, the C2f component is integrated into the feature pyramid network to the lightweight pattern; ultimately, Focal-EIoU is exerted to replace loss function of the YOLOv7 pattern to solve the divination box and the problem of wrong amplification of length and width, improving the accuracy of defect classification and positioning. The outcomes reveal that the precision of the elevated algorithm has been boosted by about 4.3%, the recall rate has reached 78.3%, and mAP increased by 3.4%. This technique exhibits high accuracy and efficiency in detecting surface defects on the tape.