Steel surface defect detection algorithm based on YOLOv8-DIC

Steel is the cornerstone of human life and production, and its quality is crucial. Detecting defects on steel surfaces using AI has become increasingly important. Considering the complex structure and diverse defect types of steel surfaces, as well as issues like lighting and environmental interference, we propose a steel surface defect detection algorithm based on YOLOv8-DIC. The main improvements include: first, integrating the DCNv4 module, which introduces deformable convolution to effectively recognize geometric transformations of objects in spatial scales and sizes, thereby enhancing model accuracy and reducing model size; second, incorporating the IRMA attention module to better focus on features at different scales and improve detection accuracy by fusing multi-scale information; finally, integrating the CSPPC module to utilize partial convolutions for handling redundant feature map computations, reducing redundancy and memory access, significantly shrinking the model size while improving accuracy. Experimental results on the NET-DET dataset show an average precision mAP of 78.7%. The model size decreased by 16%, and the improved algorithm achieved increases of 4.8% in precision, 1.5% in recall, and 3.3% in average precision, with reductions in false positives and false negatives. This enhancement greatly enhances the capability of steel surface defect detection while meeting real-time requirements.