论文摘要
Despite rapid developments in visual image-based road detection, robustly identifying road areas in visual images remains challenging due to issues like illumination changes and blurry images. To this end, LiDAR sensor data can be incorporated to improve the visual image-based road detection,because LiDAR data is less susceptible to visual noises. However,the main difficulty in introducing LiDAR information into visual image-based road detection is that LiDAR data and its extracted features do not share the same space with the visual data and visual features. Such gaps in spaces may limit the benefits of LiDAR information for road detection. To overcome this issue, we introduce a novel Progressive LiDAR adaptation-aided road detection（PLARD） approach to adapt LiDAR information into visual image-based road detection and improve detection performance. In PLARD, progressive LiDAR adaptation consists of two subsequent modules: 1) data space adaptation, which transforms the LiDAR data to the visual data space to align with the perspective view by applying altitude difference-based transformation; and 2) feature space adaptation, which adapts LiDAR features to visual features through a cascaded fusion structure. Comprehensive empirical studies on the well-known KITTI road detection benchmark demonstrate that PLARD takes advantage of both the visual and LiDAR information, achieving much more robust road detection even in challenging urban scenes. In particular, PLARD outperforms other state-of-theart road detection models and is currently top of the publicly accessible benchmark leader-board.
论文目录
文章来源
类型: 期刊论文
作者: Zhe Chen,Jing Zhang,Dacheng Tao
来源: IEEE/CAA Journal of Automatica Sinica 2019年03期
年度: 2019
分类: 信息科技,工程科技Ⅱ辑
专业: 汽车工业,电信技术
单位: IEEE,the UBTECH Sydney Artificial Intelligence Centre and the School of Computer Science, Faculty of Engineering and Information Technologies, University of Sydney,the School of Automation, Hangzhou Dianzi University,the University of Technology Sydney
基金: supported by Australian Research Council Projects(FL-170100117,DP-180103424,IH-180100002),National Natural Science Foundation of China(NSFC)(61806062)
分类号: U463.6;TN958.98
页码: 693-702
总页数: 10
文件大小: 1386K
下载量: 34
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