Aerial image object detection method based on improved YOLOv5

Qinxian Hou; Jiarui Ni; Xiaoyang Hu

doi:10.1117/12.3025652

28 February 2024 Aerial image object detection method based on improved YOLOv5

Qinxian Hou, Jiarui Ni, Xiaoyang Hu

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130712G (2024) https://doi.org/10.1117/12.3025652
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

The object features from the drone's perspective are poor, there is a lot of noise, the object scale changes drastically, and densely arranged objects, which brings huge challenges to object detection, and there are problems such as missed detection and false detection. Based on this, this article proposes an improved YOLOv5 UAV image object detection method. This method first introduces the RepVGG structure to deeply mine and enrich the semantic information of different features to alleviate the problems of poor object features and excessive noise in complex backgrounds; then it introduces the SKAttention attention mechanism to improve feature differences such as object occlusion, background interference, and multi-scale objects. representation ability under various circumstances; finally, the F-EIOU regression loss function is introduced to improve the regression speed and improve the recognition accuracy of noisy objects. Extensive experiments were conducted on the VisDrone2019 UAV aerial photography data set. Experimental results show that the average accuracy of the improved YOLOv5 (mAP@0.5) increased by 5.4%, and mAP@0.5:0.9 increased by 3.4%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qinxian Hou, Jiarui Ni, and Xiaoyang Hu "Aerial image object detection method based on improved YOLOv5", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130712G (28 February 2024); https://doi.org/10.1117/12.3025652

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Object detection

Unmanned aerial vehicles

Convolution

Computer vision technology

Convolutional neural networks

Feature fusion

Semantics

Keywords/Phrases

Search In:

Publication Years