Assisting Wind Turbine Hoisting with Yolov7 and Object Tracking Technology
Authors: 
Chenzhe Ma, Bo Fang, Hao Liu, Shun Li, and Minmin MaAuthors Info & Claims
Chenzhe Ma, Bo Fang, Hao Liu, Shun Li, and Minmin MaAuthors Info & ClaimsNovember 2023
Pages 401 - 408
Abstract
In the process of hoisting wind turbines, the driver of the crane cannot observe the panoramic view of the components being hoisted and is completely dependent on the scheduling of the commander, which restricts the efficiency and safety of hoisting. We propose an assisted hoisting algorithm for wind turbines. A high-definition camera is installed at the hoisting site to capture the hoisting scene in real time, and then Yolov7/ Yolov7-tiny is used to detect the object being hoisted in the frame. The box of the object is used as the input of the Deepsort algorithm to track the hoisting object in real time. Finally, the tracking information is used to adjust the angles of the pan and tilt and the focal length of the camera to provide the crane driver with a clear hoisting panorama. Experiments and engineering applications show that the mAP of Yolov7 reaches 0.88(Yolov7-tiny reaches 0.81), and the real-time tracking speed exceeds 12 FPS, which meets the requirements of use.
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Index Terms
- Assisting Wind Turbine Hoisting with Yolov7 and Object Tracking Technology
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Published In
November 2023
902 pages
ISBN:9798400716485
DOI:10.1145/3653081
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 May 2024
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IoTAAI 2023
IoTAAI 2023: 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence
November 24 - 26, 2023
Nanchang, China
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