Algorithm for smoking detection based on upgraded YOLACT

Qinghuan Xu; Hongbin Ma; Yuxin Wang; Jing Zhang

doi:10.1117/12.3048102

3 October 2024 Algorithm for smoking detection based on upgraded YOLACT

Qinghuan Xu, Hongbin Ma, Yuxin Wang, Jing Zhang

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132720D (2024) https://doi.org/10.1117/12.3048102
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

Smoking in public areas poses significant risks to both individual health and the well-being of bystanders. Given the microscopic nature of smoke particles, detecting them can be challenging. To address the problems of its low detection accuracy, easy to be obscured and ignored, we propose a segmentation approach based on YOLACT_SMOKE. In this study, we enhance the original YOLACT model by integrating the smoother Mish activation function, which optimizes model speed without sacrificing performance. Additionally, we replace the Res2Net module with the ResNet module to improve feature extraction and expression. Furthermore, we introduce the CBAM attention mechanism to the C3 and C4 layers to mitigate noise interference. Recognizing the lack of suitable datasets, we curated a self-made smoking dataset for training and evaluation purposes. Experimental results demonstrate a notable 4.8% increase in accuracy at IoU thresholds of 0.5 and 0.75 compared to the pre-upgrade model, with respective improvements of 5.3% and 6.7% over the original model.

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

Citation Download Citation

Qinghuan Xu, Hongbin Ma, Yuxin Wang, and Jing Zhang "Algorithm for smoking detection based on upgraded YOLACT", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132720D (3 October 2024); https://doi.org/10.1117/12.3048102

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