High-Speed Semantic Segmentation Based on Dual-Path Feature Fusion Codec Structure

Abstract

To address the problem of large parameters and slow segmentation speed in high-precision image semantic segmentation, a semantic segmentation deep learning-based model is proposed. By encoding both semantic and spatial paths, the model is able to fuse different feature information and compensate the disadvantage that it is difficult to combine spatial and semantic information, so that the feature map can be convolved efficiently. Furthermore, this model decoder merges high-level semantic information and low-level spatial information, to effectively compensate for the loss of feature information via the down-sampling operation during encoding. The experimental results on the Cityscapes and Camvid datasets show that the parameter of the model is only 3.91×10⁶, with mean intersection over union of 67.7% and 65.8% on the two datasets respectively. Also, the segmentation speeds are 111 FPS and 86 FPS, respectively. Compared with some similar models, the proposed model has fewer parameters and higher accuracy, and its segmentation speed significantly exceeds the minimum frames per second (24 FPS) required for real-time semantic segmentation.