Abstract Detecting pavement distress in a timely manner is crucial for urban safety and higher traffic efficiency. Owing to their high accuracy and real-time capabilities, object detection methods have emerged as effective solutions for road condition monitoring. However, striking a balance between detection performance and model lightweighting remains a significant challenge. To address this, we propose a novel pavement distress detection model based on YOLOv11n. First, the Ghost module is employed to significantly reduce parameters while preserving detection performance. Then, a novel and versatile high-resolution feature extraction module (HR) is designed to enhance spatial feature extraction and improve the utilization of high-resolution features. In addition, two dedicated feature extraction modules are introduced to optimize gradient flow and expand the receptive field. They enable more accurate detection of detailed damage. To further enhance multi-scale feature integration, an improved fusion structure is incorporated, where max pooling is used to effectively aggregate contextual information. Experimental results demonstrate that the proposed model improves detection accuracy, with mAP50 increasing from 71.3% to 72.9%, and mAP50–95 rising from 42.3% to 43.9%. Meanwhile, the model maintains a lightweight design, reducing parameters and model size by 13.57% and 8.81%, respectively—resulting in only 2.23M parameters and a 4.76MB model size. In addition, the model achieves an average inference time of 8.8 ms per image, enabling real-time performance. Moreover, it shows strong generalization ability on the real-world Nanjing urban dataset, achieving a competitive trade-off between accuracy and efficiency, making it suitable for deployment in resource-constrained environments.
Wang et al. (2025) studied this question.