Fusion YOLOv8s and Dynamic Convolution Algorithm for Steel Surface Defect Detection

The detection of surface defects in steel is a prerequisite for improving steel quality. When detecting surface defects in steel, the texture features of defective areas often show significant differences from the symmetry patterns of normal areas. To address the issues of low accuracy and slow recognition speed in existing steel surface defect detection methods, this study proposes an improved defect detection method based on YOLOv8s. To focus on the information of asymmetric areas in images and amplify the model’s capacity to learn target defects, we integrate the ODConv (Omni-Dimensional Dynamic Convolution) module into the backbone feature extraction network. This module infuses attention within the convolution process, augmenting the feature extraction capacity of the backbone network. Furthermore, to refine the regression speed of target boxes and enhance positioning accuracy, we adopt the WIoU (Wise Intersection over Union) bounding box loss function, featuring a dynamic non-monotonic focusing mechanism. Experimental results on the NEU-DET dataset reveal that the improved YOLOv8s-OD model achieves a 4.5% accuracy improvement compared to the original YOLOv8s, with an mAP of 78.9%. The model demonstrates robust performance in steel surface defect detection. With a modest size of only 21.5 MB, the model sustains a high detection speed of 89FPS, elevating detection accuracy while preserving real-time performance. This renders the model highly applicable in real-world industrial scenarios.