To address the significant morphological differences in road surface damage and the unsatisfactory segmentation effects under complex environmental interference in UAV inspections, we propose an improved model, MDPR-DeepLabV3+, for road surface damage segmentation. Firstly, the original backbone network is replaced with the MobileNetV2 network to enhance operational efficiency. Secondly, a DFSP module is constructed in the encoder to efficiently integrate local details and global context through progressive feature accumulation and cross-scale attention interaction. In addition, a PSA_M attention module is added to strengthen the information of damaged edges. Finally, a residual channel decoupling (RCD) module is proposed in the decoder to promote the complementarity of deep and shallow layer information and enhance feature diversity. The experimental results demonstrate that the proposed model achieves mIoU, mPA, and mPrecision of 78.47%, 92.03% and 83.93%, respectively, on a self-made UAV inspection dataset for road damage. The effectiveness of this model is further validated on the publicly available dataset Crack500, indicating strong performance in terms of pavement damage recognition accuracy and robustness in complex environments.