In recent years, with the acceleration of urbanization, urban drainage systems often struggle to cope with extreme weather, and road waterlogging occurs frequently. To solve the road waterlogging detection problem, this paper proposes an improved algorithm based on the DeepLabv3+ model. Firstly, a weighted bidirectional feature pyramid network (BiFPN) module is designed at the decoder side, which utilizes the different scales of low-level feature mapping obtained from the backbone network for fusion, giving full play to the potential of the multi-scale information obtained from the backbone network. Secondly, the Mamba-improved Transformer module is utilized to design parallel branches to process high-level feature mappings, construct global dependencies, and compensate for the possible local information loss caused by dilated convolution in ASPP. Finally, the polarized self-attention (PSA) module is introduced to mitigate the possible different effects of the direct addition of two-branch outputs on the data. The experimental results show that on the road waterlogging dataset, the improved algorithm has an mIoU of 87.54% and a PA of 96.61%, which is an improvement of 4.22% in terms of mIoU and 1.66% in terms of PA compared with the original algorithm.