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Real-time multidimensional detection of longitudinal tears in conveyor belts using FPGA-based parallel acceleration

Published online by Cambridge University Press:  06 December 2024

Fei Li Open the ORCID record for Fei Li [Opens in a new window] ,
Kun Hu  and
Hangbin Zheng
Fei Li*
Affiliation:
College of Mechanical Engineering, Donghua University, Shanghai, 201620, China
Kun Hu
Affiliation:
School of Mechanical Engineering, Anhui University of Science and Technology, Huainan, 232001, China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan, 232001, China
Hangbin Zheng
Affiliation:
College of Mechanical Engineering, Donghua University, Shanghai, 201620, China
*
Corresponding author: Fei Li; Email: [email protected]
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Abstract

In the continuous transportation process of coal in mining, exploring real-time detection technology for longitudinal tear of conveyor belts on mobile devices can effectively prevent transport failures. To address the challenges associated with single-dimensional detection, high network complexity, and difficulties in mobile deployment for longitudinal tearing detection in conveyor belts, we have proposed an efficient parallel acceleration method based on field-programmable gate arrays (FPGA) for the ECSMv3-YOLO network, which is an improved version of the you only look once (YOLO) network, enabling multidimensional real-time detection. The FPGA hardware acceleration architecture of the customized network incorporates quantization and pruning methods to further reduce network parameters. The convolutional acceleration engines were specifically designed to optimize the network’s inference speed, and the incorporation of dual buffers and multiple direct memory access channels can effectively mitigate data transfer latency. The establishment of a multidimensional longitudinal tear detection experimental device for conveyor belts facilitated FPGA acceleration experiments on ECSMv3-YOLO, resulting in model parameters of 6.257 M, mean average precision of 0.962, power consumption of 3.2 W, and a throughput of 15.56 giga operations per second (GOP/s). By assessing the effects of different networks and varying light intensity, and comparing with CPU, GPU, and different FPGA hardware acceleration platforms, this method demonstrates significant advantages in terms of detection speed, recognition accuracy, power consumption, and energy efficiency. Additionally, it exhibits strong adaptability and interference resilience.

Keywords

conveyor belt longitudinal tearmultidimensional detectionECSMv3-YOLOFPGA Acceleratorconvolutional acceleration engines
Type
Research Article
Information
AI EDAM , Volume 38 , 2024 , e19
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Copyright
© The Author(s), 2024. Published by Cambridge University Press

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