LDM-YOLO11: A Lightweight Steel Surface Defect Detection Method Based on LCAF-D and Hierarchical Mish Activation

Abstract

Lightweight detectors for steel surface defects still struggle to balance robust feature representation with deployment efficiency when defects exhibit weak textures, fine details, and large variation in scale and shape. To narrow this gap, this study develops an incremental lightweight enhancement of YOLO11n rather than proposing a brand-new detection framework. The method combines two coordinated changes. First, an improved lightweight channel-aware fusion module with a dilated branch, termed LCAF-D, is inserted into the neck to strengthen context-aware multi-scale aggregation at low additional cost. Second, a hierarchical Mish activation strategy is introduced only in selected deep backbone layers and neck downsampling layers so that nonlinear modeling is strengthened without disturbing shallow low-level feature extraction. Experiments on NEU-DET indicate that the proposed design provides competitive lightweight detection performance. The benefit becomes more evident as the input resolution increases. Under the 640 × 640 setting, YOLO11n + LCAF-D + Mish reaches 77.1% mAP@0.5 and 44.3% mAP@0.5:0.95, exceeding the YOLO11n baseline. Five-seed repeated experiments further show slightly better mean accuracy with stable variation, and runtime benchmarking shows that the method keeps lightweight characteristics with only modest increases in parameters, GFLOPs, latency, and GPU memory. Additional evaluation on GC10-DET under the same 640 × 640 protocol also gives an overall improvement over the baseline, although the gains remain category-dependent rather than universal. Overall, the method is best understood as a competitive lightweight engineering refinement for steel surface defect detection, particularly when the input resolution is sufficient to preserve subtle defect details.