Research on detection algorithm based on improved Yolov5

Mingsheng Liu; Yurong Mo; Yu Wang

doi:10.1117/12.3029614

21 June 2024 Research on detection algorithm based on improved Yolov5

Mingsheng Liu, Yurong Mo, Yu Wang

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131671Z (2024) https://doi.org/10.1117/12.3029614
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

In order to improve the effect and quality of video surveillance, we hope to use cutting-edge technology to realize video event recognition and other functions, and enhance the ability to respond to emergencies. Firstly, this paper briefly introduces the network framework, data enhancement processing method and loss function of YOLO series algorithm and YOLOv5 model. Further, it briefly explains the improvement of the improved YOLOv5 in this paper compared with the previous YOLOv5 version, including the enhancement strategy of the data set, the equalization processing strategy of the data set, the processing of the data characteristics, the improvement of the backbone network, and the tuning of the loss function. Finally, the number of classified samples of the training data set is introduced and the experimental results of the trained YOLOv5 are analyzed and visualized.

Citation Download Citation

Mingsheng Liu, Yurong Mo, and Yu Wang "Research on detection algorithm based on improved Yolov5", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131671Z (21 June 2024); https://doi.org/10.1117/12.3029614

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