关键词: 语义分割, DeepLabV3+, 带状池化, 有效通道注意力, 深度可分离卷积

Abstract: The current high-precision semantic segmentation medel generally have the problems of high computational complexity and large memory usage, so it is difficult to deploy on embedded platforms with limited hardware storage and computing power. Aiming at the problem, an improved efficient semantic segmentation medel  based on improved DeepLabV3+ is proposed by comprehensively considering three aspects of network parameters, calculation and performance. The model uses MobileNetV2 as the backbone network, and combines the mix strip pooling(MSP) in the atrous spatial pyramid pooling (ASPP) module to obtain dense context information. The effective channel attention (ECA) module is introduced in the decoder to restore a clearer target boundary. Depthwise separable convolution is applied to the ASPP module and decoder to compress the model. Experiment on the PASCAL VOC 2012 dataset show that the number of network parameters of the medel  is 4.5×106, the number of floating point operations is 11.13 GFLOPs, and the mean intersection over union is 72.07%, which proves that the algorithm achieves the good balance between calculation efficiency and segmentation accuracy.

Key words: semantic segmentation, DeepLabV3+, strip pooling, efficient channel attention, depthwise separable convolution