Feature Extraction and Enhancement for Real‐time Semantic Segmentation

Most of the semantic segmentation real‐time networks improve the segmentation speed by reducing the spatial resolution, leading to the accuracy being significantly reduced as a result. To solve this problem, we propose feature enhancement module (FEM), feature extraction and fusion module (FEFM). By extracting and enhancing the future map before the image down‐sample on the backbone and fusing the low‐level features with rich details and the high‐level features with more semantic information. Based on the FEM and FEFM, we introduce a real‐time semantic segmentation network feature extraction and enhancement network. In the experiment, using Cityscapes and CamVid datasets, the proposed network achieves a balance between computing speed and accuracy. Without additional processing and pretraining, it achieves 75.47% Mean IoU on the Cityscapes test dataset with only 29.96G Flops and a speed of 94 frames per second on a single RTX 2080Ti card. Code is available at https://github.com/favoMJ/FEENet.