LCFNet: Loss Compensation Fusion Network for Real-time Semantic Segmentation of Urban Road Scenes

Semantic segmentation is used by intelligent transportation systems to understand and sense the traffic environment. However, achieving semantic segmentation in realtime is a challenge due to the necessity of both high accuracy and fast processing. This is especially valuable for applications such as autonomous driving and industrial robotics. In this paper, we propose a real-time semantic segmentation network, called LCFNet, which makes use of three-branch structure. The LCFNet consists of Lightweight Detail Guidance Fusion (L-DGF) and Lightweight Semantic Guidance Fusion (L-SGF) modules. Both modules aggregate information from various network layers. In the termination of network, a Total Guidance Fusion (TGF) module is proposed for processing information from all three branches. Depth-wise Convolution Pyramid Pooling (DCPP) module is also included to optimize accuracy and simplify computation. The effectiveness of LCFNet is demonstrated on two typical semantic segmentation datasets, Cityscapes and CamVid. On a single NVIDIA GeForce GTX 2080 Ti GPU, LCFNet reaches 77.02% mIoU at 95.97 FPS and 81.17% mIoU at 204.82 FPS, respectively.