research-article

UAV Lightweight Object Detection Based on the Improved YOLO Algorithm

Authors:

Chao Xu,

Lisang LiuAuthors Info & Claims

EITCE '21: Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering

Pages 1502 - 1506

https://doi.org/10.1145/3501409.3501674

Published: 31 December 2021 Publication History

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Abstract

Aiming at the characteristics of small objects in low-altitude images, special shooting angles, and variable shooting angles of unmanned aerial vehicles (UAVs), this paper proposes a structural innovation based on the YOLOv5-MobileNetv3Small network model. It transplants the MobileNetv3 network structure and improves the BackBone network structure to solve the problem of inference high-pixel images taking up too much memory for low-power edge computing nodes. The improved YOLO algorithm uses the Visdrone2019-DET dataset to train the network model and uses the Jetson NX edge computing platform on the UAV to process 1920*1080 video streams for testing. The memory usage of the optimized YOLOv5-MobileNetv3Small network model can be reduced by 72.4%.

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Боровий ОІлечко Р(2024)Структура системи автономного управління безпілотним літальним апаратом засобами комп'ютерного зоруScientific Bulletin of UNFU10.36930/4034050934:5(69-77)Online publication date: 23-May-2024
https://doi.org/10.36930/40340509
Yan HWang JKong XTomiyama H(2024)YOLO-ELD: Efficient and Lightweight Detection for UAV Aerial Imagery2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831605(4151-4156)Online publication date: 6-Oct-2024
https://doi.org/10.1109/SMC54092.2024.10831605
Xiao LLi WYao SLiu HRen D(2024)High-precision and lightweight small-target detection algorithm for low-cost edge intelligenceScientific Reports10.1038/s41598-024-75243-114:1Online publication date: 9-Oct-2024
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Index Terms

UAV Lightweight Object Detection Based on the Improved YOLO Algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

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EITCE '21: Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering

October 2021

1723 pages

ISBN:9781450384322

DOI:10.1145/3501409

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 31 December 2021

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EITCE 2021

EITCE 2021: 2021 5th International Conference on Electronic Information Technology and Computer Engineering

October 22 - 24, 2021

Xiamen, China

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EITCE '21 Paper Acceptance Rate 294 of 531 submissions, 55%;

Overall Acceptance Rate 508 of 972 submissions, 52%

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https://doi.org/10.36930/40340509
Yan HWang JKong XTomiyama H(2024)YOLO-ELD: Efficient and Lightweight Detection for UAV Aerial Imagery2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831605(4151-4156)Online publication date: 6-Oct-2024
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Xiao LLi WYao SLiu HRen D(2024)High-precision and lightweight small-target detection algorithm for low-cost edge intelligenceScientific Reports10.1038/s41598-024-75243-114:1Online publication date: 9-Oct-2024
https://doi.org/10.1038/s41598-024-75243-1
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Automatic Pose Estimation of Micro Unmanned Aerial Vehicle for Autonomous Landing

YOLO series algorithms in object detection of unmanned aerial vehicles: a survey

YOLO-ERF: lightweight object detector for UAV aerial images

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