Aiming at the problems of too large a number of parameters and insufficient segmentation accuracy of semantic segmentation network on mobile intelligent terminals,an improved DDeepLabV3+network algo-rithm was proposed.First,the depth-separable MobileNet structure is used as the backbone of the network to reduce the number of parameters and complexity of the network,thereby effectively reducing the running time.Secondly,low-level features of the network are introduced to achieve multi-scale information fusion and reduce the spatial information loss caused by network downsampling.Finally,the network ASPP structure is designed based on the attention mechanism to enhance the utilization of feature extraction in experiments.The optimized network structure significantly reduces the calculation time while maintaining high classification accuracy.In the Cityscapes data set used in the study,the average intersection and union ratio of the network increased by 2.37%,while in the Camvid dataset,the ratio increased by 2.13%.
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