CAR-YOLO: A Lightweight Improved YOLOv11 Model for Pavement Defect Detection

Abstract

Pavement deterioration poses substantial technical-economic burdens and brings notable social-environmental risks: degraded road surfaces increase vehicle maintenance expenditures, raise traffic accident probabilities, lower transportation operational efficiency, and produce extra carbon emissions caused by aggravated fuel consumption. Automatic pavement defect detection is therefore a vital technical prerequisite for timely road maintenance and mitigating those adverse consequences. However, existing methods suffer from two prominent limitations: missed detection of subtle defects under complex scenarios and high model complexity. To tackle the above limitations, this paper proposes CAR-YOLO, a lightweight pavement defect detection algorithm built upon an improved YOLOv11n. First, a convolution-based context-guided module is incorporated to enhance the exploitation of defect contextual information, thus improving the feature representation quality and detection accuracy of subtle defects. Second, the original neck network is replaced with the attention scale sequence fusion framework (ASF), which fuses multi-scale features to boost detection performance for subtle, complex-texture pavement defects. Finally, the lightweight RepViT network is introduced into the backbone network to streamline the model architecture and reduce model complexity while maintaining detection precision. Experimental results demonstrate that the CAR-YOLO model achieves an mAP50 of 63.8% on the RDD2022 Chinese UAV dataset, approximately 5% higher than the baseline YOLOv11n. The parameter count is reduced from 2.58 M to 1.71 M, corresponding to a compression ratio of 33.6%; the computational load drops from 6.3 G to 4.4 G, a reduction of around 30.2%. While delivering higher detection accuracy, the proposed algorithm effectively reduces the parameter scale and computational overhead.