Research on lightweight detection model of spontaneous combustion of electric vehicles

Chengyu Lu; Jie Yang; Hui Zhou

doi:10.1117/12.3005925

10 October 2023 Research on lightweight detection model of spontaneous combustion of electric vehicles

Chengyu Lu, Jie Yang, Hui Zhou

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 1279955 (2023) https://doi.org/10.1117/12.3005925
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

The article addresses the problems of large models, high cost, high false detection rate, low detection accuracy, and insufficient computing power of edge computing platforms that are common in current smoke and fire detection systems for parking lot fire monitoring. In this paper, we propose a lightweight self-ignition detection improvement model that is easy to use on monitoring devices and edge computing platforms. The model uses the YOLOv5 model as the base network, introduces an adaptive co-concern module, enhances the smoke and fire feature extraction capability of the backbone network, and uses sub-pixel sampling for semantic enhancement of the depth feature map. The size of the improved model is only 3.8MB, which can be more lightly deployed in edge surveillance environment for monitoring. The simulation comparison experiments before and after the model improvement are conducted in the self-combustion dataset under the self-built parking lot surveillance scenario with 3977 images in the training set and 896 images in the test set. The experimental results show that the error rate of the improved model for self-combustion smoke and fire detection is 8.1%, which is 2.7% lower than the error detection rate before the improvement; the detection accuracy reaches 82.0%, which is 5.2% higher than the accuracy before the improvement.

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

Citation Download Citation

Chengyu Lu, Jie Yang, and Hui Zhou "Research on lightweight detection model of spontaneous combustion of electric vehicles", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 1279955 (10 October 2023); https://doi.org/10.1117/12.3005925

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

Target detection

Combustion

Performance modeling

Feature extraction

Systems modeling

Deep learning

Show All Keywords

Keywords/Phrases

Search In:

Publication Years