TC-Net: Transformer-Convolutional Networks for Road Segmentation

Zhenyu Wu, Chaohui Song, Haibin Yan

Abstract: In this paper, we propose a lightweight backbone network called Transformer-Convolutional Networks (TC-Net) for ef-ficient road segmentation. Conventional road segmentation methods employ data fusion to enrich key features, so that more branches are required in the network which increases the parameter size of the model and the difficulty of deploy-ment on edge devices. On the contrary, our TC-Net fully uti-lizes the interconnection between various regions on the input image to enhance the feature representation ability while re-ducing the amount of parameters of each branch. The key components of our TC-Net are the Transformer-Conv (TC) and PatchMerging-Conv (PC) modules. Specifically, the TC module applies convolution to optimize the process of cross-window connections, and the PC module flexibly adjusts the scale of feature maps to further reduce the computational cost. Extensive experiments on the KITTI dataset show that our method greatly reduces the amount of parameters while achieving comparable performance with state-of-the-arts.