Enhanced Automatic Root Recognition and Localization in GPR Images Through a YOLOv4-Based Deep Learning Approach-Reference-Cited by-同舟云学术

Enhanced Automatic Root Recognition and Localization in GPR Images Through a YOLOv4-Based Deep Learning Approach

Published:2022 Issue: Volume:60 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Li Shupeng¹^ORCID,Cui Xihong²^ORCID,Guo Li³^ORCID,Zhang Luyun¹,Chen Xuehong²^ORCID,Cao Xin²^ORCID

Affiliation:

1. Academy of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China

2. State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing, China

3. State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09791296.pdf?arnumber=9791296

Reference58 articles.

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2. Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

3. YOLOv3: An incremental improvement;redmon,2018

4. YOLOv4: Optimal speed and accuracy of object detection;bochkovskiy;Proc CVPR,2020

5. A Novel Method of Hyperbola Recognition in Ground Penetrating Radar (GPR) B-Scan Image for Tree Roots Detection

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