DCSN: Deformable Convolutional Semantic Segmentation Neural Network for Non-Rigid Scenes

Abstract

This paper presents a novel semantic segmentation network for outdoor and unstructured scenarios for autonomous driving based on deformable convolution and geometric distortion pipelines. The semantic segmentation tasks for autonomous driving are generally designed for the urban scene, city-view, and highly structured scenarios, such as the CityScapes dataset, KITTI, and BDD, while rare study focuses on outskirts scenarios. Therefore, the performance of existing semantic segmentation networks on such datasets might be unreliable. To conquer this issue, a novel densely connected residual block (DCRB) with the deformable convolution is proposed to form our backbone for capturing the non-rigid feature representation. In this way, the gradient flow of our DCRB could be better back-propagated from the segmentation head, resulting in a stable training process. Second, geometric distortion augmentation is introduced in the data augmentation pipeline, simulating the possible deformation situations in real-world outdoor scenarios. The experiments are conducted that the proposed semantic segmentation network significantly outperforms the state-of-the-art methods for both Cityscapes and Outdoor scenarios.