Abstract: For complex tasks such as urban scene segmentation,there are problems such as low utilization of feature map space information,inaccurate segmentation boundaries,and excessive network parameters.To solve these problems,DualSeg,a scene segmentation model based on dual learning,is proposed.Firstly,depthwise separable convolution is used to significantly reduce the number of model parameters Secondly,accurate context information is obtained by fusing hollow pyramid pooling and double attention mechanism modules.Finally,dual learning is used to construct a closed-loop feedback network,and the mapping space is constrained by duality,while training the two tasks of “image scene segmentation” and “dual image reconstruction”,it can assist the training of the scene segmentation model,help the model to better perceive the category boundary and improve the recogni-tion ability.Experimental results show that the DualSeg model based on the Xception skeleton network achieves 81.3% mIoU and 95.1% global accuracy on natural scene segmentation dataset PASCAL VOC,respectively,and the mIoU reaches 77.4% on the CityScapes dataset,and the number of model parameters decreases by 18.45%,which verifies the effectiveness of the model.A more effective attention mechanism will be explored in the future to further improve the segmentation accuracy.

Key words: Scene segmentation, Image reconstruction, Dual learning, Attention mechanism, Depthwise separable convolution, Multi-level feature fusion