Lightweight road distress detection model LRDD-YOLOv8 for efficient and accurate detection in complex scenarios

Abstract High-precision road distress detection (RDD) systems often face challenges in balancing accuracy, efficiency, and generalisation while managing high operational costs. To address these issues, this study proposed a lightweight RDD you only look once-based model (LRDD-YOLOv8) optimised for practical deployment. Key innovations include the C2f-faster module integrated into the neck network, the LS-detect head utilizing group normalisation and shared convolution to minimise computational overhead, and the Wise-minimum points distance intersection over union (MPDIoU) loss combines Wise-IoU and MPDIoU accelerating convergence. LRDD-YOLOv8 reduced parameters by 32.9% (from 3.01 M to 2.02 M) and floating operations by 28.4% (from 8.1 G to 5.8 G) compared to YOLOv8n, while improving mAP@0.5 by 1.3% (64.4% vs. 63.1%) on street view datasets deployed on the SenseTime BOXER-8120AI edge computing platform, LRDD-YOLOv8 achieved a 34.2% faster inference speed (55.5 ms vs. 84.3 ms per image) with maintained accuracy, demonstrating suitability for real-time applications. The model exhibited robust generalisation in complex backgrounds and effectively suppressed duplicate detections. These advancements position LRDD-YOLOv8 as a cost-effective solution for resource-constrained road maintenance tasks. Future efforts will focus on expanding distress categories and improving illumination-invariant feature learning to enhance adaptability.