Cigarette detection in diverse environments using YOLO-CD with adaptive multiscale feature extraction

Wei He; Qiming Li

doi:10.1117/12.3055958

9 January 2025 Cigarette detection in diverse environments using YOLO-CD with adaptive multiscale feature extraction

Wei He, Qiming Li

Author Affiliations +

Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 134861E (2025) https://doi.org/10.1117/12.3055958
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China

Abstract

Cigarette detection plays a crucial role in environmental protection and public health. However, current deep learning based algorithms for small object detection often suffer from poor accuracy and are highly susceptible to environmental interference and variations in lighting conditions. To address these challenges, this paper introduces an end-to-end cigarette detection algorithm: YOLO-CD. First, a novel adaptive multi-scale feature extraction module is designed to more comprehensively capture features across different scales. Second, to mitigate the distortion and loss of small object information in deep networks, a more flexible and efficient downsampling design is employed. Additionally, a deep supervision mechanism is introduced by adding auxiliary detection branches in the intermediate layers of the network, effectively enhancing the model's ability to capture small object features without increasing computational complexity. Experimental results demonstrate that YOLO-CD outperforms existing mainstream methods on public datasets, achieving a 3.0% improvement over the baseline model while also reducing the number of parameters and computational load.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wei He and Qiming Li "Cigarette detection in diverse environments using YOLO-CD with adaptive multiscale feature extraction", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 134861E (9 January 2025); https://doi.org/10.1117/12.3055958

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