Improved lightweight vehicle detection algorithm based on YOLOv5s

Qinghao Ran

doi:10.1117/12.3032890

5 July 2024 Improved lightweight vehicle detection algorithm based on YOLOv5s

Qinghao Ran

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131846S (2024) https://doi.org/10.1117/12.3032890
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Aiming at the current challenges of vehicle detection algorithms, such as complex models, large parameter sizes, and high requirements for hardware calculation, this paper introduces a lightweight improved YOLOv5 algorithm, which maintains high accuracy while remarkably reducing the number of model parameters. Firstly, the Ghost lightweight module is adopted to reconstruct the backbone network, reducing model parameters and enhancing inference speed. Subsequently, simAM, a parameter-free attention module, is incorporated into the neck network to improve algorithm accuracy and suppress environmental interference. Finally, the NMS algorithm is improved into DioU-NMS, combining the penalty term and centroid distance to minimize the probability of miss. The experimental results indicate that, compared with the original YOLOv5s algorithm, the proposed algorithm showcases a mere 0.8% reduction in the average precision (AP) value, the model parameters are dropped by 48%, and the computational workload is decreased by 49.7%, realizing the lightweight of the algorithm.

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

Citation Download Citation

Qinghao Ran "Improved lightweight vehicle detection algorithm based on YOLOv5s", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131846S (5 July 2024); https://doi.org/10.1117/12.3032890

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