An algorithm for detecting smoke and dust in open-pit coal mines based on YOLOv5

Sijie Lu; Liyong Zhou; Di Gao; Baoshan Li; Qi Li; Yongxing Du

doi:10.1117/12.3035351

19 July 2024 An algorithm for detecting smoke and dust in open-pit coal mines based on YOLOv5

Sijie Lu, Liyong Zhou, Di Gao, Baoshan Li, Qi Li, Yongxing Du

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132132G (2024) https://doi.org/10.1117/12.3035351
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Coal mining in open-pit mines can lead to spontaneous combustion due to the accumulation of coal. During the early stages of spontaneous combustion, smoke is generated, and its diffusion characteristics are similar to the dust stirred up by vehicles in the coal yards. The diffusion of smoke not only affects the safety of coal production but also poses health risks to the staff. Currently, most smoke detection algorithms focus on fire backgrounds, including both flames and smoke targets. However, there is limited research on smoke detection in the environment of open coal yards. The unique characteristics of the coal yard environment, such as the easy diffusion of smoke and dust with no distinct boundaries, make smoke detection challenging. The conventional smoke detection algorithms are not suitable for detecting smoke targets in coalfield environments. To address this issue, this paper proposes the M-ST-YOLOv5 model (Mobile-SwinTransformer-YOLOv5). Firstly, incorporates the concept of the MobileNetV2 network architecture to reduce the complexity of practical deployment. Secondly, the Swin-Transformer module is added to enhance the network's recognition capabilities for smoke and dust. Experimental results demonstrate that the proposed method effectively reduces the number of network parameters while improving the smoke detection rate.

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

Citation Download Citation

Sijie Lu, Liyong Zhou, Di Gao, Baoshan Li, Qi Li, and Yongxing Du "An algorithm for detecting smoke and dust in open-pit coal mines based on YOLOv5", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132132G (19 July 2024); https://doi.org/10.1117/12.3035351

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
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Object detection

Convolution

Mining

Detection and tracking algorithms

Target recognition

Education and training

Head

Show All Keywords

Keywords/Phrases

Search In:

Publication Years