Research on indoor target ranging based on improved Yolov5

Ting Wang; Bin Tan

doi:10.1117/12.3045048

16 January 2025 Research on indoor target ranging based on improved Yolov5

Ting Wang, Bin Tan

Author Affiliations +

Proceedings Volume 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024); 134474A (2025) https://doi.org/10.1117/12.3045048
Event: International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 2024, Wuhan, China

Abstract

Visual object detection and ranging is a technology that uses computer vision technology to detect and distance target objects in images or videos. This technology plays an important role in the field of industrial automation. In this paper, based on the improved yolov5 algorithm, the adaptive receptive field convolution module is introduced in target detection, and the triangulation principle in the traditional method is used to achieve target detection and ranging, so as to achieve more accurate target detection and improve ranging accuracy. The accuracy of the system can be verified by many experiments. The experimental results show that the ranging error of the system is less than 5% in the range of 0.2~1m.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ting Wang and Bin Tan "Research on indoor target ranging based on improved Yolov5", Proc. SPIE 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 134474A (16 January 2025); https://doi.org/10.1117/12.3045048

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