BiSeNet has been proved to be a popular two-stream network for real-time
segmentation. However, its principle of adding an extra path to encode spatial
information is time-consuming, and the backbones borrowed from pretrained
tasks, e.g., image classification, may be inefficient for image segmentation
due to the deficiency of task-specific design. To handle these problems, we
propose a novel and efficient structure named Short-Term Dense Concatenate
network (STDC network) by removing structure redundancy. Specifically, we
gradually reduce the dimension of feature maps and use the aggregation of them
for image representation, which forms the basic module of STDC network. In the
decoder, we propose a Detail Aggregation module by integrating the learning of
spatial information into low-level layers in single-stream manner. Finally, the
low-level features and deep features are fused to predict the final
segmentation results. Extensive experiments on Cityscapes and CamVid dataset
demonstrate the effectiveness of our method by achieving promising trade-off
between segmentation accuracy and inference speed. On Cityscapes, we achieve
71.9% mIoU on the test set with a speed of 250.4 FPS on NVIDIA GTX 1080Ti,
which is 45.2% faster than the latest methods, and achieve 76.8% mIoU with 97.0
FPS while inferring on higher resolution images.

提出了一种高效的 STDC 网络和 Detail Aggregation 模块，用于实时图像分割，通过在低级别层中学习空间信息，并将低级和深层特征融合以预测最终的分割结果，取得了较高的分割准确率和快速推理速度。