A YOLOv8-DTG-Based Framework for Pavement Distress Detection

Abstract Pavement condition is critical to traffic safety and pavement service life. Existing pavement distress detection algorithms are plagued by challenges such as low detection accuracy and inefficiency. To address these issues, this study proposes YOLOv8-DTG, a lightweight pavement distress detection algorithm based on the YOLOv8 framework. Specifically, dynamic snake convolution is integrated into the initial stages of the network, and the triplet attention mechanism is introduced to enhance feature extraction capabilities while minimizing the number of model parameters. Additionally, GSConv and VoV-GSCSP modules are introduced to reduce computational complexity and enhance feature interaction. Experimental results on the RDD 2022 dataset demonstrate that YOLOv8-DTG achieves an mAP50 of 0.729, representing an absolute improvement of 2.1 percentage points over the baseline YOLOv8 model. Additionally, YOLOv8-DTG reduces both model parameters and computational load. This study provides promising application prospects for real-time pavement distress detection in practical road maintenance scenarios.