U-Net Based Road Area Guidance for Crosswalks Detection from Remote Sensing Images-Reference-Cited by-同舟云学术

U-Net Based Road Area Guidance for Crosswalks Detection from Remote Sensing Images

Published:2021-03-22 Issue: Volume: Page:1-17
ISSN:0703-8992
Container-title:Canadian Journal of Remote Sensing
language:en
Short-container-title:Canadian Journal of Remote Sensing

Author:

Chen Ziyi¹,Luo Ruixiang¹²,Li Jonathan³^ORCID,Du Jixiang¹,Wang Cheng²

Affiliation:

1. Department of Computer Science and Technology, Fujian Key Laboratory of Big Data Intelligence and Security, Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University, Xiamen, Fujian, China

2. School of Information Science and Engineering, Xiamen University, Xiamen, Fujian, China

3. Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, N2L 3G1, Canada

Funder

Natural Science Foundation of Fujian Province

National Natural Science Foundation of China

United National Natural Science Foundation of China

Special National Key Research and Development Plan

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/07038992.2021.1894915

Reference39 articles.

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4. Bochkovskiy A., Wang C.Y., and Liao H.Y.M. 2020. “YOLOv4: Optimal Speed and Accuracy of Object Detection.” arXiv:2004.10934

5. Cao, Z., Li, X., and Zhao, L. 2019. "Object detection in VHR image using transfer learning with deformable convolution." Paper presented at the IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, July 2019.

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