Research on Semantic Segmentation Algorithm Based on Lightweight DeepLabV3+ Network

Abstract

Abstract —This paper presents an improved version of the DeepLabV3+ network to address issues such as large parameter count, difficulties in mobile deployment, limited receptive field, and insufficient utilization of low-level semantic information in existing deep learning semantic segmentation networks. The main enhancement approach is as follows: we utilize the lightweight MobileNetV2 as the backbone feature extraction network, while an improved multi-scale atrous convolution module (AS-ASPP) and convolutional block attention mechanism (CBAM) are introduced. Tests conducted on the PASCAL VOC 2012 dataset demonstrate that the optimized model retains merely around one-tenth the parameters of the original network, while attaining superior segmentation precision and computational effectiveness. Specifically, it reaches a mIoU of 73.21% and a Precision of 80.56%, with the training time reduced by approximately 50% and the inference speed significantly improved.