U-Net Based Road Area Guidance for Crosswalks Detection from Remote Sensing Images. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Chen, Ziyi; Luo, Ruixiang; Li, Jonathan; Du, Jixiang; Wang, Cheng
• Is Part Of: Canadian journal of remote sensing. Volume 47:Issue 1(2021)
• Page Start: 83
• Page End: 99
• Publication Date: 2021-01-02
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2021.1894915
• Languages: English
• ISSNs: 0703-8992

Abstract

Abstract: Due to the wide distribution of crosswalks over the road nets, the finding of impaired crosswalk marks is usually long-time delayed, which may put crosswalk pedestrians into danger. To reduce the repairing cost and improve the finding speed of damaged crosswalks, this paper uses remote sensing images to automatically detect crosswalks. The detection results can be used for further examination of crosswalks. However, the detection of crosswalks from remote sensing images suffers from serious interferes of many other kinds of ground targets. Besides, there are rare openly available datasets for the research of crosswalk detection from remote sensing images. To conquer the above problems, this study provides an openly available dataset for the research of crosswalk detection. To improve the robustness, we propose a crosswalk detection framework which uses a U-Net based road area guidance. First, we use CNN models to detect crosswalks. Then, we use U-Net to extract potential road areas. Third, we propose a mixture classification strategy which combines the detection confidence and potential road area guidance for final crosswalk detection. Experimental results show that the road area guidance for crosswalks' detection is effective and can improve the detection performance.