More Web Proxy on the site http://driver.im/

research-article

The drone detection based on improved YOLOv5

Authors:

Weiying NieAuthors Info & Claims

AICCC '22: Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference

Pages 83 - 89

https://doi.org/10.1145/3582099.3582113

Published: 20 April 2023 Publication History

Abstract

The wide application of drones not only brings convenience to production and life, but also poses a threat to public safety. Therefore, the detection of s is crucial. However, tiny drones make it difficult to cope with traditional detection methods such as radar and photoelectricity because of their tiny size. Therefore, this paper proposed a tiny drones detection method based on YOLOv5 framework. By optimizing the size of Anchor box, embedding the Convolutional Block Attention Module (CBAM) and optimized loss function (CIoU), the detection performance of the original algorithm for drones under complex background is improved. The improved YOLOv5 algorithm is trained and tested on the self-built dataset, and its mean Average Precision, Accuracy and Recall reach 96.9%, 97.8% and 95.6% respectively. Finally, the improved YOLOv5 is used for drone detection in complex background environments. Compared with the original algorithm, it can correctly identify drone targets in harsh environments.

References

[1]

QU X T, ZHUANG D Y, XIE H B. 2020. Detection Methods for Low-Slow-Small (LSS) UAV. J. COMMAND CONTROL& Simulation 42, 2 (2020), 128-135.

[2]

ZHANG S F. 2017. Study for Low-Altitude Slow and Small Target Radar Detecting. J. Modern Navigation 8, 6 (2017), 436-440.

[3]

A. Krizhevsky, I. Sutskever, and G. E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. J. ACM 60, 5 (May 2012), 84–90. https://doi.org/10.1145/3065386.

Digital Library

[4]

R. Girshick, J. Donahue, T. Darrell, and J. Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Conf. Compute. Vis. Pattern Recognition, 580-587

[5]

S. Ren, K. He, R. Girshick, and J. Sun. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst, 2015, 91–99.

[6]

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. 2016. SSD: Single shot multibox detector. Eur. Conf. Comput. Vis. Cham, Switzerland: Springer, 21–37.

[7]

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. 2016. You only look once: Unified, real-time object detection. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 779–788.

[8]

J. Redmon and A. Farhadi. 2017. YOLO9000: Better, faster, stronger. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 6517–6525.

[9]

J. Redmon and A. Farhadi. 2018. YOLOv3: An incremental improvement. arXiv:1804.02767.

[10]

LI Q Z, XIONG R R, WANG R P, 2019. Research on Real-time UAV Recognition Method Based on SSD Algorithm. J. Ship Electronic Engineering 39, 5 (2019), 30-35.

[11]

MA Q, ZHU B, ZHANG H W, 2019. Low-Altitude UAV Detection and Recognition Method Based on Optimized YOLOv3. J. Laser & Optoelectronics Progress 56, 20 (2019), 279-286

[12]

Bao W Q, XIE L Q, XU C H, 2022. A Real-time Detection Method of Micro UAV based on YOLOv5. J. Journal of Ordnance Equipment Engineering (2022), https://kns.cnki.net/kcms/detail/50.1213.TJ.20220414.1458.008.html

[13]

S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon. 2018. CBAM: Convolutional Block Attention Module. in Computer Vision – ECCV 2018, 3–19. https://doi.org/10.1007/978-3-030-01234-21

[14]

D. Chen and D. Miao. 2021. Control Distance IoU and Control Distance IoU Loss Function for Better Bounding Box Regression. arXiv, http://arxiv.org/abs/2103.11696

[15]

T.Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie. 2017. Feature Pyramid Networks for Object Detection. arXiv (Apr. 19, 2017), http://arxiv.org/abs/1612.03144.

[16]

J. Yang, X. Fu, Y. Hu, Y. Huang, X. Ding, and J. Paisley. 2017. PanNet: A Deep Network Architecture for Pan-Sharpening. 2017 IEEE International Conference on Computer Vision (ICCV), 1753–1761. https://doi.org/10.1109/ICCV.2017.193.

[17]

H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese. 2019. Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression. arXiv(Apr 2019), http://arxiv.org/abs/1902.09630.

Cited By

Noor ALi KTovar EZhang PWei B(2024)Fusion flow-enhanced graph pooling residual networks for Unmanned Aerial Vehicles surveillance in day and night dual visionsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108959136:PBOnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108959

Index Terms

The drone detection based on improved YOLOv5
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

Recommendations

Mask Wearing Detection in Dim Lighting Conditions based on Improved YOLOv5
ICECC '23: Proceedings of the 2023 6th International Conference on Electronics, Communications and Control Engineering

The COVID-19 epidemic is still rampant around the world. Wearing masks can effectively block the spread of novel coronavirus, while mask wearing detection can timely remind people in public places to wear masks. In order to solve the problem of low ...
Anomaly Detection Model for Key Places Based on Improved YOLOv5
Artificial Intelligence and Security
Abstract
In recent years, key places such as underground stations and train stations, which are crowded and highly mobile, have become key targets for abnormal behaviour such as violence by some extremists or violent elements. The public safety risks in ...
UAV small target detection algorithm based on an improved YOLOv5s model
Abstract
The targets of UAV target detection are usually small targets, and the backgrounds are complex. In this work, aiming at the problem that small targets are easy to be missed or misdetected during the UAV detection, an improved YOLOv5s_MSES target ...
Highlights
- Improved YOLOv5s_MSES algorithm for small target detection in UAV aerial photography.
- Introduction of Small target detection module STD to enhance detection of small targets.
- Multi-scale feature fusion module for improved accuracy ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

AICCC '22: Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference

December 2022

302 pages

ISBN:9781450398749

DOI:10.1145/3582099

Copyright © 2022 ACM.

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 the author(s) 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

AICCC 2022

AICCC 2022: 2022 5th Artificial Intelligence and Cloud Computing Conference

December 17 - 19, 2022

Osaka, Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
62
Total Downloads

Downloads (Last 12 months)27
Downloads (Last 6 weeks)3

Reflects downloads up to 20 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Noor ALi KTovar EZhang PWei B(2024)Fusion flow-enhanced graph pooling residual networks for Unmanned Aerial Vehicles surveillance in day and night dual visionsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108959136:PBOnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108959

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents