Imaging-Based Instance Segmentation of Pavement Cracks Using an Improved YOLOv8 Network

Abstract

An improved YOLOv8 model, YOLOv8-NETC, is proposed in this study for fine-grained crack recognition through instance segmentation. YOLOv8-NETC is designed and enhanced with four self-developed modules. First, ablation studies were conducted to assess the effectiveness of each module. The model’s accuracy and speed were evaluated based on parameters such as mean average precision (mAP50) and model weight (MW). The experimental results show significant improvements in accuracy, storage efficiency, and processing speed. Compared to the original network, YOLOv8-NETC achieved a 6.5% increase in mAP50, a 6.1% average reduction in MW and parameters, and an 8.5% improvement in FPS. Subsequently, YOLOv8-NETC was compared with other state-of-the-art models across three datasets, including the crack type dataset, crack trueness dataset, and the public Crack500 dataset. The experimental results demonstrate that the proposed model achieved the best recognition performance on all datasets. Furthermore, YOLOv8-NETC showed superior robustness against interference and computational efficiency compared to other benchmark models.