A YOLOv11 Empowered Road Defect Detection Model

Roads inevitably have defects during use, which not only seriously affect their service life but also pose a hidden danger to traffic safety. Existing algorithms for detecting road defects are unsatisfactory in terms of accuracy and generalization, so this paper proposes an algorithm based on YOLOv11. The method embeds wavelet transform convolution (WTConv) into the backbone’s C3k2 module to enhance low-frequency feature extraction while avoiding parameter bloat. Secondly, a novel multi-scale fusion diffusion network (MFDN) architecture is designed for the neck to strengthen cross-scale feature interactions, boosting detection precision. In terms of model optimization, the traditional downsampling method is discarded, and the innovative Adown (adaptive downsampling) technique is adopted, which streamlines the parameter scales while effectively mitigating the information loss problem during downsampling. Finally, in this paper, we propose Wise-PIDIoU by combining WiseIoU and MPDIoU to minimize the negative impact of low-quality anchor frames and enhance the detection capability of the model. The experimental results indicate that the proposed algorithm achieves an average detection accuracy of 86.5% for mAP@50 on the RDD2022 dataset, which is 2% higher than the original algorithm while ensuring that the amount of computation is basically unchanged. The number of parameters is reduced by 17%, and the F1 score is improved by 3%, showing better detection performance than other algorithms when facing different types of defects. The excellent performance on embedded devices proves that the algorithm also has favorable application prospects in practical inspection.