Lightweight distress detection models for asphalt pavement based on optimized detection head structure

Abstract:

Pavement distresses are typically characterized by irregular morphology, weak texture features, dense small targets, and large-scale variations. Traditional detection heads suffer from limited spatial modeling, poor scale perception, and insufficient semantic sharing, making it difficult to balance detection accuracy and computational efficiency. To address these issues, this study proposed three lightweight detection head optimization schemes based on the YOLOv11 framework, establishing an intelligent and efficient detection architecture for pavement distresses. Specifically, the DyHead module introduces a multi-dimensional attention mechanism to achieve dynamic feature modeling, significantly enhancing scale and spatial perception; the LSCD structure adopts a lightweight shared convolution design, reducing the number of parameters by 30% and FLOPs by 22% while maintaining feature representation capacity, thereby improving deployment efficiency; and the GLSA module integrates a global-local spatial attention mechanism to strengthen feature discrimination and multi-scale adaptability under complex road conditions. Experimental results demonstrate that all three improved models achieve notable gains in both accuracy and efficiency: DyHead achieves an mAP@0.5 of 64.95%, LSCD performs best under resource-constrained conditions, and GLSA exhibits the most balanced overall performance. The proposed methods provide new insights for lightweight model design and embedded deployment of pavement distress detection, offering valuable technical support for the intelligent maintenance of smart transportation infrastructure.