YOLO-LSDI: An Enhanced Algorithm for Steel Surface Defect Detection Using a YOLOv11 Network

Addressing the difficulties in identifying surface defects in steel and various industrial materials, including challenges in detection, low generalization, and poor robustness, as well as the shortcomings of existing algorithms for industrial applications, this paper presents the YOLO-LSDI algorithm for steel surface defect identification. First, the model integrates the Adaptive Multi-Scale Pooling–Fast (AMSPPF) module, an adaptive multi-scale pooling approach that improves the extraction of global semantic and local edge features. Second, the Deformable Spatial Attention Module (DSAM), a hybrid attention mechanism combining deformable and spatial attention, is introduced to enhance the network’s focus on defect-relevant regions under complex industrial backgrounds. Third, Linear Deformable Convolution (LDConv) replaces standard convolution to better adapt to the irregular shapes of defects while maintaining low computational cost. Finally, the Inner-Complete Intersection over Union (Inner-CIoU) loss function is adopted to improve localization accuracy and training stability. Experimental results on the NEU-DET dataset demonstrate a 5.8% improvement in the mAP@0.5, a 2.4% improvement in the mAP@0.5:0.95, and a 6.2% improvement in the F1-score compared to the YOLOv11n baseline, with GFLOPs reduced to 6.1 and inference speed reaching 162.1 frames per second (FPS). Evaluations on the GC10-DET dataset, APSPC dataset, and a PCB defect dataset further confirm the generalization capability of YOLO-LSDI, with mAP@0.5 improvements of 4.2%, 2.1%, and 3.1%, and corresponding mAP@0.5:0.95 improvements of 1.1%, 1.5%, and 1.3%, respectively. These results validate the effectiveness and practicality of the proposed model for real-time industrial defect-detection tasks.