The detection of steel material defects is vital for enhancing product quality, safety, and reliability. However, conventional deep learning approaches, such as YOLOv7 and SSD, suffer from slow detection speed and suboptimal accuracy. To address these issues, we present an enhanced YOLOv7 algorithm for steel defect detection. Our approach optimizes the YOLOv7 model by integrating ResNet Channel Attention Connection modules into the backbone network to improve feature extraction capabilities. Furthermore, a self-Coordinate Attention mechanism is introduced to enhance detection accuracy. Additio...
