A Lightweight Real-Time Pavement Distress Detection Network with Multi-Scale Coordinate Attention and Multi-Granularity Knowledge Distillation

Automated pavement distress detection is essential for transportation infrastructure maintenance and road asset management. In practice, however, such detectors often need to run on embedded devices mounted on inspection vehicles, and two challenges hinder real-world deployment: (1) cracks and potholes possess markedly different geometric priors—thin, elongated topology versus blob-like shapes—so a generic backbone tends to under-represent at least one class, and (2) accuracy-oriented detectors are typically too heavy for the embedded GPUs commonly mounted on inspection platforms. To address these issues, this paper proposes a lightweight real-time pavement distress detection network. First, a multi-scale coordinate attention (MSCA) module is embedded in the neck so that long-range row/column-wise dependencies are encoded together with multi-scale local context, which is helpful for slender cracks while remaining computationally efficient. Second, a slender-aware detection head (SADH) couples a 1×k/k×1 asymmetric branch with the standard square branch, giving the head an explicit inductive bias for elongated objects. Third, a multi-granularity knowledge distillation (MGKD) scheme is designed, which transfers teacher knowledge from a heavier teacher to the proposed student at three complementary granularities—pixel-level attention-masked features, instance-pair relations, and decoupled class-prior logits—thereby covering the three distinct levels of information that a multi-class dense detector relies on. The network is trained and evaluated on the public RDD2022 benchmark together with a supplementary in-house set of asphalt potholes. Under the fixed-seed, single-run evaluation used in this study, the proposed method achieves an mAP@0.5 of 71.65% on the author-defined test split, which is not directly comparable with evaluations on the official RDD2022 test set, with the comparison restricted to seven representative baselines evaluated under the same protocol, and runs at 72.5 FPS on an NVIDIA Jetson Orin Nano. Although its latency is modestly higher than that of YOLOv8s, it retains real-time inference capability for on-vehicle pavement inspection.