Research on YOLOV7-tiny fruit detection algorithm improved by α-IoU

Zhanpeng Jing; Liucun Zhu

doi:10.1117/12.3014681

9 January 2024 Research on YOLOV7-tiny fruit detection algorithm improved by α-IoU

Zhanpeng Jing, Liucun Zhu

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 1296905 (2024) https://doi.org/10.1117/12.3014681
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

In response to the problems of low detection accuracy and inaccurate positioning in traditional YOLO algorithms for fruit detection tasks, this paper proposes an improved method - replacing the original loss function of YOLOV7 with the AlphaIoU loss function, and optimizing the boundary box regression of the model. This can accelerate the convergence speed of the model and improve training accuracy. Through experimental verification, it was found that the improved YOLOV7-tiny model significantly improved the average accuracy, accuracy, and recall in fruit detection tasks compared to the original network. This indicates that this method has good performance in solving the problems of traditional YOLO algorithm, providing better performance and accuracy for fruit detection tasks, and contributing to the further development of applications and research in related fields.

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

Citation Download Citation

Zhanpeng Jing and Liucun Zhu "Research on YOLOV7-tiny fruit detection algorithm improved by α-IoU", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 1296905 (9 January 2024); https://doi.org/10.1117/12.3014681

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