DRMNet: more efficient bilateral networks for real-time semantic segmentation of road scenes

Semantic segmentation is crucial in autonomous driving because of its accurate identification and segmentation of objects and regions. However, there is a conflict between segmentation accuracy and real-time performance on embedded devices. We propose an efficient lightweight semantic segmentation network (DRMNet) to solve these problems. Employing a streamlined bilateral structure, the model encodes semantic and spatial paths, cross-fusing features during encoding, and incorporates unique skip connections to coordinate upsampling within the semantic pathway. We design a new self-calibrated aggregate pyramid pooling module (SAPPM) at the end of the semantic branch to capture more comprehensive multi-scale semantic information and balance its extraction and inference speed. Furthermore, we designed a new feature fusion module, which guides the fusion of detail features and semantic features through attention perception, alleviating the problem of semantic information quickly covering spatial detail information. Experimental results on the CityScapes, CamVid, and NightCity datasets demonstrate the effectiveness of DRMNet. On a 2080Ti GPU, DRMNet achieves 78.6% mIoU at 88.3 FPS on the CityScapes dataset, 78.9% mIoU at 149 FPS on the CamVid dataset, and 53.5% mIoU at 160.4 FPS on the NightCity dataset. These results highlight the model's ability to balance accuracy and real-time performance better, making it suitable for embedded devices in autonomous driving applications.