中文版 | English
Title

Farmland Obstacle Detection from the Perspective of UAVs Based on Non-local Deformable DETR

Author
Corresponding AuthorZenghong,Ma; Xiaoguang,Liu
Publication Years
2022-11-23
DOI
Source Title
ISSN
2077-0472
EISSN
2077-0472
Volume12Issue:12Pages:1983
Abstract

In precision agriculture, unmanned aerial vehicles (UAVs) are playing an increasingly important role in farmland information acquisition and fine management. However, discrete obstacles in the farmland environment, such as trees and power lines, pose serious threats to the flight safety of UAVs. Real-time detection of the attributes of obstacles is urgently needed to ensure their flight safety. In the wake of rapid development of deep learning, object detection algorithms based on convolutional neural networks (CNN) and transformer architectures have achieved remarkable results. Detection Transformer (DETR) and Deformable DETR combine CNN and transformer to achieve end-to-end object detection. The goal of this work is to use Deformable DETR for the task of farmland obstacle detection from the perspective of UAVs. However, limited by local receptive fields and local self-attention mechanisms, Deformable DETR lacks the ability to capture longrange dependencies to some extent. Inspired by non-local neural networks, we introduce the global modeling capability to the front-end ResNet to further improve the overall performance of Deformable DETR. We refer to the improved version as Non-local Deformable DETR. We evaluate the performance of Non-local Deformable DETR for farmland obstacle detection through comparative experiments on our proposed dataset. The results show that, compared with the original Deformable DETR network, the mAP value of the Non-local Deformable DETR is increased from 71.3% to 78.0%. Additionally, Non-local Deformable DETR also presents great performance for detecting small and slender objects. We hope this work can provide a solution to the flight safety problems encountered by UAVs in unstructured farmland environments.

Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
First ; Corresponding
Funding Project
[32001424] ; [JCYJ20210324102401005]
WOS Research Area
Agriculture
WOS Subject
Agronomy
WOS Accession No
WOS:000900216300001
Publisher
Data Source
人工提交
Publication Status
在线出版
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/416289
DepartmentSUSTech Institute of Microelectronics
Affiliation
1.School of Microelectronics, Southern University of Science and Technology, Shenzhen 518005, China
2.Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518005, China
3.School of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China
4.Key Laboratory of Transplanting Equipment and Technology of Zhejiang Province, Hangzhou 310018, China
First Author AffilicationSUSTech Institute of Microelectronics
Corresponding Author AffilicationSUSTech Institute of Microelectronics
First Author's First AffilicationSUSTech Institute of Microelectronics
Recommended Citation
GB/T 7714
Dashuai,Wang,Zhuolin,Li,Xiaoqiang,Du,et al. Farmland Obstacle Detection from the Perspective of UAVs Based on Non-local Deformable DETR[J]. Agriculture-Basel,2022,12(12):1983.
APA
Dashuai,Wang,Zhuolin,Li,Xiaoqiang,Du,Zenghong,Ma,&Xiaoguang,Liu.(2022).Farmland Obstacle Detection from the Perspective of UAVs Based on Non-local Deformable DETR.Agriculture-Basel,12(12),1983.
MLA
Dashuai,Wang,et al."Farmland Obstacle Detection from the Perspective of UAVs Based on Non-local Deformable DETR".Agriculture-Basel 12.12(2022):1983.
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File Name/Size DocType Version Access License
agriculture-12-01983(4196KB) Restricted Access--
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