Abstract: Scene segmentation is crucial for computers to understand the road environment， the large semantic segmentation model based on deep learning can often achieve excellent segmentation performance， but it cannot be flexibly deployed on edge devices because of its large number of parameters and computation. To solve this problem， this paper proposes an efficient scene semantic segmentation model E-SegNet from the perspective of lightweight. Firstly， the lightweight feature extraction model EfficientNet-B0 is used as the encoder of the model to extract the hierarchical features. Then， CPAM and CCAM modules based on the self-attention mechanism are used to establish the dependency between the single element in the deep features and the global central element in the two dimensions of spatial and channel. Finally， the feature of deep and shallow layers are fused and the final prediction results are output. Experimental results on video frame data set Camseq01 show that the proposed E-SegNet model achieves better segmentation performance with less than 1/10 of the parameters of DeeplabV3+ model and about 1/4 of the computational effort， which reflects the effectiveness of the model， and provides more schemes for deploying lightweight models on edge devices.

Key words: deep learning, lightweight, scene segmentation, spatial attention, channel attention