Research on pavement distress detection based on the YOLO26 model

Abstract

To address the challenge that the diversity and complex morphology of pavement distresses make it difficult for traditional detection methods to simultaneously achieve high accuracy and real-time performance, a multi-class pavement distress detection approach based on the YOLO26 model is proposed. Seven common types of distresses, including block cracking, longitudinal cracking, transverse cracking, potholes, strip patching, block patching, and raveling, are considered as detection targets. A high-quality annotated dataset comprising 12,451 images is constructed. Experimental results show that YOLO26 achieves a precision of 0.80, a recall of 0.81, and an mAP@0.5 of 0.79 on the test set. These results outperform those of the comparative models, including YOLOv5, YOLOv8, and YOLO11. In particular, YOLO26 improves mAP@0.5 by 1.3% compared to YOLO11, while maintaining real-time inference speed. Visualization results further validate the model’s capability for accurate localization and classification of multiple distress types. This study provides a reliable and efficient technical solution for precise multi-class pavement distress detection.