TBNet: Three-Branch Real-Time Semantic Segmentation Network that Can Be Easily Built from Most Lightweight Backbone

Traditional deep learning semantic segmentation networks can already achieve high segmentation accuracy, but they generally suffer from large parameter size and slow inference speed, making it challenging to deploy them on mobile embedded devices for real-time segmentation tasks. However, the real-time segmentation networks that have emerged in recent years have proved their effectiveness and efficiency, with major advancements in lightweight encoders or decoders and multi-branch architectures. Taking their various advantages, we propose a real-time semantic segmentation network based on a general lightweight backbone and multi-scale feature fusion, which uses sharing shallow features, including semantic, spatial, and boundary three-branch decoder and a global boundary attention fusion module–GBAU . We use different backbone models to conduct experiments for verifying its outstanding performance. Experimental results show that when using STDC1 as the backbone and an input size of 512*1024, this model achieves 72.95% mIoU on the Cityscapes dataset. The model has 8.17M parameters and the inference speed can reach 194.7 FPS, so achieve good balance between segmentation accuracy, inference speed and parameter size.