MMS-YOLOv10: A fast and improved pavement surface defect detection model based on YOLOv10

Abstract Pavement defect detection greatly affects pavement service life and vehicle operation safety. Current pavement defect detection models encounter difficulties in accurately detecting minor defects, handling imbalanced class samples, and maintaining speed. To overcome these issues, we propose a fast and improved pavement surface defect detection model named MMS-YOLOv10, which is based on YOLOv10n. This model includes three significant improvements. First, we incorporate the multidimensional collaborative attention (MCA) mechanism into the C2f module of the backbone network to enhance adaptability to objects of different scales and improve the feature extraction ability. Second, we design a multilevel feature fusion (MFF) module to enhance semantic and detailed information and improve the feature expression ability of the model with different levels of features. Third, the sample correlated weighting loss function is introduced during network training to solve the issue of sample imbalance through the dynamic weight mechanism. The performance of the MMS-YOLOv10 model is assessed through experiments conducted on the famous RDD2022 dataset. The qualitative and quantitative results show that the proposed model can lead to promising improvements in detection accuracy. Through further ablation experiments, the components of the proposed model are validated to achieve performance improvements.