Lightweight road crack detection by fusing multiscale features and attention mechanisms

Abstract

Accurate road crack detection is essential for intelligent pavement inspection, yet thin crack morphology, cluttered backgrounds, and deployment constraints still challenge lightweight detectors. This paper presents an improved YOLOv11s-based detector for road distress recognition. Three coordinated modules are introduced: a C3k2- SHSA-CGLU backbone block for stronger contextual perception and dynamic crack-feature filtering, a GLSABiFPN neck for bidirectional multi-scale fusion with enhanced fine-detail retention, and a lightweight shared-convolution detection head for compact prediction. Experiments on the China subset of RDD2022 show that the proposed method improves mAP@0.5 from 87.2% to 89.4% and reduces parameters from 9.41 M to 7.32 M compared with YOLOv11s. Additional cross-dataset results on GRDDC2020 indicate acceptable generalization, while the reduced parameter count and compact model size suggest good deployment potential. Overall, the method provides a practical balance between detection accuracy and model compactness for automated pavement inspection.