Improving the key point detection algorithm for railway operations based on YOLOv7-tiny

Wenjing Zheng; Tianci Yang; Haotian Sun; Jun Li; Fei Lin; Xuehan Zheng; He Gao

doi:10.1117/12.3054607

20 December 2024 Improving the key point detection algorithm for railway operations based on YOLOv7-tiny

Wenjing Zheng, Tianci Yang, Haotian Sun, Jun Li, Fei Lin, Xuehan Zheng, He Gao

Author Affiliations +

Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 134211K (2024) https://doi.org/10.1117/12.3054607
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China

Abstract

In recent years, deep learning algorithms have been widely applied across many industries. It has also seen rapid development in the field of key point detection. This paper proposes an improved YOLOv7-tiny algorithm for detecting key points in railway workers’ operations, aiming to assist workers and reduce errors in their tasks. To enhance the model's detection capabilities, three optimization methods are proposed. Deformable Convolution (DCNv2) is introduced into the feature fusion module to enhance the model's ability to extract and fuse features. Next, to address the issues of object classification and localization, a lightweight decoupled head is introduced. Finally, to improve the accuracy of detecting targets with significant scale differences and enhance the model's convergence speed, we introduce SIoU as the loss function. Experimental results show that the improved algorithm achieves an accuracy (mAP@0.5) of 91.1% and a detection precision (P) of 87.3&percnt, representing increases of 1.5&percnt and 4.8&percnt, respectively, compared to the YOLOv7-tiny model. The detection rate is 132 FPS, which meets the requirements for practical detection.

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

Citation Download Citation

Wenjing Zheng, Tianci Yang, Haotian Sun, Jun Li, Fei Lin, Xuehan Zheng, and He Gao "Improving the key point detection algorithm for railway operations based on YOLOv7-tiny", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 134211K (20 December 2024); https://doi.org/10.1117/12.3054607

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

Head

Detection and tracking algorithms

Target detection

Convolution

Deformation

Data modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years