Steel Strip Quality Assurance With YOLOV7-CSF: A Coordinate Attention and SIoU Fusion Approach

Steel strip can develop surface defects during manufacturing and processing, affecting structural integrity and usability. These defects can be caused by both internal and external factors. However, traditional manual error detection techniques do not meet today’s accuracy standards. Therefore, an improved version of the YOLOv7 algorithm for steel strip surface defect detection is proposed in this work. A lightweight and inexpensive Coordinate Attention (CA) mechanism is built into the structure of the head of YOLOv7. The SCYLLA-Intersection over Union (SIoU) loss function is used to improve detection efficiency. Furthermore, to enhance the dataset, a vertical flip augmentation technique is applied to create the optimal model:YOLOv7-CSF through fusion of CA and SIoU. It has been observed in the experimental findings that the modified YOLOv7-CSF algorithm’s mAP value in the detection is 4.09% better than that of the original YOLOv7 method, reaching 66.1% and a maximum of 96.9% accuracy in a single category of defects. The efficacy and superiority of the updated model are shown by comparing it with the recently announced YOLOv8, other steel strip datasets and other hyper-parameter tuned models, providing a novel way for daily surface defect detection on steel strips.