Hyperspectral image target recognition based on YOLO model

Jia-ju Ying; Shi-qu Yin; Wei-yong Yang; Hui Liu; Xu Li

doi:10.1117/12.3016421

30 April 2024 Hyperspectral image target recognition based on YOLO model

Jia-ju Ying, Shi-qu Yin, Wei-yong Yang, Hui Liu, Xu Li

Proceedings Volume 13156, Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition; 131560Q (2024) https://doi.org/10.1117/12.3016421
Event: Sixth Conference on Frontiers in Optical Imaging Technology and Applications (FOI2023), 2023, Nanjing, JS, China

Abstract

Hyperspectral image target recognition, as an important component of computer vision, has broad application prospects in the military field. We conducted research on hyperspectral image target recognition technology based on deep learning, with typical military targets as the main recognition objects. A target recognition algorithm suitable for hyperspectral images is proposed based on the YOLO network model. To address the issue of the YOLO algorithm not being sensitive enough to small targets, the calculation formula for the loss function in the algorithm has been adjusted, and a multi-scale training method has been added. In response to the phenomenon of missed detections when the YOLO algorithm faces multi-objective clusters, five anchor boxes are added to each grid. In addition, the network convergence speed is accelerated by adding a BN layer; Introducing residual networks effectively prevents network degradation; Adding 1*1 convolutional layer can better extract image detail features and improve network recognition accuracy. The hyperspectral image target dataset is obtained by using hyperspectral imaging instruments to shoot multiple targets from multiple angles and directions under different lighting conditions, and labeling them. Divide the data into training and testing sets for training. The trained model was used for recognition experiments on vehicles in hyperspectral images. The experimental results showed that the model met the real-time requirements for target recognition in hyperspectral images, and performed well in the recognition performance of small targets at long distances.

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

Citation Download Citation

Jia-ju Ying, Shi-qu Yin, Wei-yong Yang, Hui Liu, and Xu Li "Hyperspectral image target recognition based on YOLO model", Proc. SPIE 13156, Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition, 131560Q (30 April 2024); https://doi.org/10.1117/12.3016421

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
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Target recognition

Hyperspectral imaging

Education and training

Detection and tracking algorithms

Deep learning

Data modeling

Image processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years