Semantic segmentation method based on improved DeeplabV3+

Abstract

The semantic segmentation model Deeplabv3+ model in deep learning has a great deal of parameters and spend plenty of training time, and the accuracy of small objects and edge segmentation is low. To solve this problem, an improved semantic segmentation algorithm for DeepLabv3+ network is proposed. Firstly, MobileNetv2 is used instead of Xception to reduce model complexity and speed up model training; secondly, the attention mechanism is introduced to improve segmentation accuracy; thirdly, a Strip Pooling branch is connected in parallel to the Atrous Spatial Pyramid Pooling module (ASPP) to improve the characteristics of the model extraction ability; finally, the Swish activation function is used to replace the Relu function. Experimental results show that compared with the original model, the proposed algorithm can still maintain a high accuracy when the number of parameters is greatly reduced.