2020/02/24 by Lei Sun, Kailun Yang, Sun, Lei +7 · 2 citations
Computer Science · Engineering · #Advanced Neural Network Applications #Autonomous Vehicle Technology and Safety #Video Surveillance and Tracking Methods #cs.CV #cs.RO #eess.IV
paper · pdf · doi:10.48550/arxiv.2002.10570
Accepted by IEEE Robotics and Automation Letters (RA-L); 8 Figures, 3 Tables; Code is available at https://github.com/AHupuJR/RFNet
arxiv created 2020/06/27 · arxiv updated 2020/06/30
Semantic segmentation has made striking progress due to the success of deep convolutional neural networks. Considering the demands of autonomous driving, real-time semantic segmentation has become a research hotspot these years. However, few real-time RGB-D fusion semantic segmentation studies are carried out despite readily accessible depth information nowadays. In this paper, we propose a real-time fusion semantic segmentation network termed RFNet that effectively exploits complementary cross-modal information. Building on an efficient network architecture, RFNet is capable of running swiftly, which satisfies autonomous vehicles applications. Multi-dataset training is leveraged to incorporate unexpected small obstacle detection, enriching the recognizable classes required to face unforeseen hazards in the real world. A comprehensive set of experiments demonstrates the effectiveness of our framework. On Cityscapes, Our method outperforms previous state-of-the-art semantic segmenters, with excellent accuracy and 22Hz inference speed at the full 2048x1024 resolution, outperforming most existing RGB-D networks.