LiteMSNet: a lightweight semantic segmentation network with multi-scale feature extraction for urban streetscape scenes

Abstract

Semantic segmentation plays a pivotal role in computer scene understanding, but it typically requires a large amount of computing to achieve high performance. To achieve a balance between accuracy and complexity, we propose a lightweight semantic segmentation model, termed LiteMSNet (a Lightweight Semantic Segmentation Network with Multi-Scale Feature Extraction for urban streetscape scenes). In this model, we propose a novel Improved Feature Pyramid Network, which embeds a shuffle attention mechanism followed by a stacked Depth-wise Asymmetric Gating Module. Furthermore, a Multi-scale Dilation Pyramid Module is developed to expand the receptive field and capture multi-scale feature information. Finally, the proposed lightweight model integrates two loss mechanisms, the Cross-Entropy and the Dice Loss functions, which effectively mitigate the issue of data imbalance and gradient saturation. Numerical experimental results on the CamVid dataset demonstrate a remarkable mIoU measurement of 70.85% with less than 5M parameters, accompanied by a real-time inference speed of 66.1 FPS, surpassing the existing methods documented in the literature. The code for this work will be made available at https://github.com/River-ding/LiteMSNet.