Steel Surface Defect Detection Based on YOLOv8-TLC

To address the issues of low accuracy and efficiency in traditional image processing algorithms for steel surface defect detection, a novel steel surface defect detection algorithm based on YOLOv8-TLC is proposed. To more accurately detect defect targets in images that are missed due to their large size, an additional scale detection layer is introduced. Meanwhile, the Large Selective Kernel (LSK) attention mechanism is incorporated to deeply explore spatial structural information that is highly relevant to the steel surface defect targets, further enhancing the model’s spatial feature extraction capabilities. A triple spatial pyramid module is also constructed to address the problem of redundant feature extraction. Additionally, the C2f-DS module is designed to ensure the acquisition of richer gradient flow information without increasing the number of parameters. Experimental results on the NEU-DET dataset show that the YOLOv8-TLC algorithm achieves a mean average precision (mAP) of 79.8%, improving the mAP by 3.2% while enhancing detection speed.