The invention belongs to the field of image processing, and discloses a trunk two-way image semantic segmentation method, which specifically comprises the following steps: inputting an image into a semantic segmentation model for feature extraction to obtain an initial feature map; after the initial feature map is processed by a residual network, performing primary semantic fusion on the initial feature map and the initial feature map subjected to down-sampling and residual network processing to obtain a primary fusion high-resolution feature map and a primary fusion low-resolution feature map; performing residual network processing on the primary fusion high-resolution feature map, and performing secondary semantic fusion on the primary fusion high-resolution feature map and a primary fusion low-resolution feature map subjected to down-sampling and residual network processing; and obtaining a third-level fusion high-resolution feature map and a third-level fusion low-resolution feature map; processing the three-level fused high-resolution feature map through a residual network; carrying out third semantic fusion on the three-level fused high-resolution feature map and the three-level fusion low-resolution feature map subjected to down-sampling and residual network processing; and obtaining a five-level fusion feature map; and carrying out up-sampling on the five-level fusion feature map through a decoder to obtain an image semantic segmentation result.
Trunk two-way image semantic segmentation method for scene understanding of mobile robot in complex environment