Steel surface defects in manufacturing are typically tiny, low-contrast, and boundary-ambiguous, especially under complex textures (e.g., rolling marks, crazing), poor illumination, and high noise. These characteristics cause frequent missed detections and localization errors, particularly for defects with large-scale variations. Existing detectors, including YOLOv11, lack sufficient local spatial modeling for deformed or blurred boundaries and suffer from limited cross-scale feature interaction, leading to suboptimal performance on industrial benchmarks. To overcome these limitations, we propose YOLOv11-LLR—a YOLOv11-based framework that jointly enhances multi-scale feature modeling and inference efficiency. YOLOv11-LLR synergistically integrates three modules: Deformable Large Kernel Attention (DLKA) for adaptive local spatial perception, Lightweight Group-wise Attention (LWGA) for cross-scale interaction, and Re-parameterized Convolution (RepConv) for deployment-friendly speed. We evaluate on two representative datasets: NEU-DET (six defect types on hot-rolled steel strips) and GC10-DET (ten defect types with higher background complexity). Compared to baseline YOLOv11, YOLOv11-LLR achieves +3.5% mAP@0.5 (80.2%→83.7%) and +2.4% mAP@0.5:0.95 (48.7%→51.1%) on NEU-DET, and larger gains of +9.8% (61.0%→70.8%) and +3.4% (33.4%→36.8%) on the more challenging GC10-DET. These results demonstrate that YOLOv11-LLR provides an effective, robust, and industrially deployable solution for steel surface defect detection under complex textures, noise, and multi-scale variations.
Li et al. (2026) studied this question.