Coal quantity detection of conveyor belt based on improved YOLOv5 algorithm

Ying Hou; Ze Zhang; Huan Shi; Lin Yang

doi:10.1117/12.2645732

23 August 2022 Coal quantity detection of conveyor belt based on improved YOLOv5 algorithm

Ying Hou, Ze Zhang, Huan Shi, Lin Yang

Proceedings Volume 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022); 123050J (2022) https://doi.org/10.1117/12.2645732
Event: International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 2022, Hangzhou, China

Abstract

Belt conveyor is the important coal production and transportation equipment. if it operations in high speed for a long time in case of no coal or little coal, it will cause a lot of power loss and belt damage. An improved yolov5 real-time coal flow detection algorithm is presented. The Swin Transfomer attention mechanism is used to improve the traditional convolutional receptive field limited problem, and the weighted feature fusion splicing method is applied to adaptively select the backbone feature extraction, so that the network structure can obtain the global semantic information of the feature map and effectively improve the detection ability. Compared with YOLOv5 algorithm, the experimental results show that the mAP performance improves by 2.1% and reduces the detection time by 10.6%, which can quickly and accurately detect the conveyor belt coal flow in real time.

Citation Download Citation

Ying Hou, Ze Zhang, Huan Shi, and Lin Yang "Coal quantity detection of conveyor belt based on improved YOLOv5 algorithm", Proc. SPIE 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 123050J (23 August 2022); https://doi.org/10.1117/12.2645732

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