LHA-Net: A Lightweight and High-Accuracy Network for Road Surface Defect Detection

Road surface defect detection can effectively reduce maintenance costs, which is a critical component in road structural health monitoring. However, existing methods often face challenges in the heavy computational parameters and high-accuracy detection, limiting their practical applicability in resource-constrained industrial settings. To alleviate this gap, we propose a Lightweight and High-accuracy Network (LHA-Net) for road surface defect detection, consisting of three sub-networks for feature extraction, feature fusion, and detection head. First, the proposed Direction-guided Global Feature-Aware Module (DGFM) and the proposed Heterokernel Local Feature-Aware Module (HLFM) are used in the feature extraction sub-net to extract global and local features while minimizing network parameters. Second, we propose an Asymptotically Weighted Aggregation Mechanism (AWAM) in the feature fusion sub-net, which efficiently merges detailed and semantic features through asymptotic multi-scale fusion and weighted fusion at multiple stages. Third, we propose a Lightweight Decoupling Head (LDH) in the detection head sub-net to extract target location and category information by emphasizing defect details in horizontal and vertical dimensions. Finally, to improve the generalizability, we propose the RDD-CC dataset extension of RDD2022 using road images collected by automobiles in China. Compared with the well-established lightweight YOLOv8n, LHA -Net achieves comparable or superior mAP@.5 scores with gains of +0.8%, +0.5%, and +0.3% on RDD-CC, RDD-SCM, and RDD-SCD datasets, respectively. Remarkably, LHA-Net does so with only 2.5 M parameters (reduced by 16%) and 5.6 GFLOPs (reduced the computational load by 30%).