Lightweight target detection method based on YOLOv5

Bo-yuan Li; Jiang-mei Zhang; Hao-lin Liu

doi:10.1117/12.3011367

8 November 2023 Lightweight target detection method based on YOLOv5

Bo-yuan Li, Jiang-mei Zhang, Hao-lin Liu

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129231A (2023) https://doi.org/10.1117/12.3011367
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

Object detection algorithms based on deep learning often adopt complex algorithm structures in pursuit of higher detection accuracy, resulting in a huge amount of overall model parameters and computation, making it very difficult to deploy the algorithm on mobile platforms with limited hardware computing power. This article studies the weight reduction of object detection models and proposes a lightweight algorithm YOLOv5-SNGS based on the YOLOv5 model. Firstly, the ShuffleBlock module is designed to perform lightweight transformation on the backbone of YOLOv5, significantly reducing the amount of model parameters and computation, and improving model detection speed. Then, the SimSPPF spatial pyramid pooling structure is used to extract targeted features from targets of different scales. In the bottleneck part, the GSConv convolution module is designed to fuse multi-channel feature information, and the SimAM attention mechanism is added to quantitatively evaluate the importance of neurons in the network. Key feature extraction is carried out for key areas of the image. The experimental results on the Pascal VOC dataset showed that compared to the benchmark model YOLOv5s, the parameter count of YOLOv5-SNGS decreased from 7.2million to 4.1million, and the computational load decreased from 16.6GFLOPs to 8.1GFLOPs. At the same time, the FPS during mobile platform deployment increased from 80 to 105, with an increase of 31%. While significantly reducing hardware computing power dependence and improving detection speed, the mAP of YOLOv5-SNGS remained at the same level with YOLOv5s, which can meet the target detection requirements of mobile hardware platforms

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bo-yuan Li, Jiang-mei Zhang, and Hao-lin Liu "Lightweight target detection method based on YOLOv5", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129231A (8 November 2023); https://doi.org/10.1117/12.3011367

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
13 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Convolution

Target detection

Performance modeling

Object detection

Feature extraction

Neurons

Data modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years