Real-Time Driving Scene Semantic Segmentation

Abstract

Real-time understanding of surrounding environment is an essential yet challenging task for autonomous driving system. The system must not only deliver accurate result but also low latency performance. In this paper, we focus on the task of fast-and-accurate semantic segmentation. An efficient and powerful deep neural network termed as Driving Segmentation Network (DSNet) and a novel loss function Object Weighted Focal Loss are proposed. In designing DSNet, our goal is to achieve the best capacity with constrained model complexity. We design efficient and powerful unit inspired by ShuffleNet V2 and also integrate many successful techniques to achieve excellent balance between accuracy and speed. DSNet has 0.9 million of parameters, achieves 71.8% mean Intersection-over-Union (IoU) on Cityscapes validation set, 69.3% on test set, and runs 100+ frames per second (FPS) at resolution 640 × 360 on NVIDIA 1080Ti. In order to improve performance on minor and hard objects which are crucial in driving scene, Object Weighted Focal Loss (OWFL) is proposed to deal with the serious class imbalance issue in pixel-wise segmentation task. It could effectively improve the overall mean IoU of minor and hard objects by increasing loss contribution from them. Experiments show that DSNet performs 2.7% points higher on minor and hard objects compared with fast-and-accurate model ERFNet under similar accuracy. These traits imply that DSNet has great potential for practical autonomous driving application.

Keywords

• Autonomous driving perception
• efficient neural networks
• semantic segmentation