Deep Dual-Resolution Networks for Real-Time and Accurate Semantic Segmentation of Traffic Scenes

Using light-weight architectures or reasoning on low-resolution images, recent methods realize very fast scene parsing, even running at more than 100 FPS on a single GPU. However, there is still a significant gap in performance between these real-time methods and the models based on dilation backbones. To this end, we proposed a family of deep dual-resolution networks (DDRNets) for real-time and accurate semantic segmentation, which consist of deep dual-resolution backbones and enhanced low-resolution contextual information extractors. The two deep branches and multiple bilateral fusions of backbones generate higher quality details compared to existing two-pathway methods. The enhanced contextual information…

• NN
  National Natural Science Foundation of China

  Awards: 62173108, U1964201, 62022031
• FR
  Fundamental Research Funds for the Central Universities