2018/12/06 by Wenfu Wang, Zhijie Pan, Wang, Wenfu +1
Computer Science · Engineering · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Video Surveillance and Tracking Methods #cs.AI #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.1812.07049
We have discovered some reported numbers unreproducible, and decided to redesign the methods, and rewrite most of the paper
openalex publication_date 2018/12/06 · arxiv created 2019/12/06 · arxiv updated 2019/12/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We focus on the very challenging task of semantic segmentation for autonomous driving system. It must deliver decent semantic segmentation result for traffic critical objects real-time. In this paper, we propose a very efficient yet powerful deep neural network for driving scene semantic segmentation termed as Driving Segmentation Network (DSNet). DSNet achieves state-of-the-art balance between accuracy and inference speed through efficient units and architecture design inspired by ShuffleNet V2 and ENet. More importantly, DSNet highlights classes most critical with driving decision making through our novel Driving Importance-weighted Loss. We evaluate DSNet on Cityscapes dataset, our DSNet achieves 71.8% mean Intersection-over-Union (IoU) on validation set and 69.3% on test set. Class-wise IoU scores show that Driving Importance-weighted Loss could improve most driving critical classes by a large margin. Compared with ENet, DSNet is 18.9% more accurate and 1.1+ times faster which implies great potential for autonomous driving application.