Hostname: page-component-669899f699-8p65j Total loading time: 0 Render date: 2025-04-30T03:39:59.441Z Has data issue: false hasContentIssue false

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*
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]

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.

Type
Research Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Adiono, T, Putra, A, Sutisna, N, et al. (2021) Low latency YOLOv3-tiny accelerator for low-cost FPGA using general matrix multiplication principle. IEEE Access 9, 141890141913.CrossRefGoogle Scholar
Bao, C, Xie, T, Feng, W, et al. (2020) A power-efficient optimizing framework fpga accelerator based on winograd for yolo. IEEE Access 8, 9430794317.CrossRefGoogle Scholar
Błażej, R, Jurdziak, L, Kozłowski, T, et al. (2018) The use of magnetic sensors in monitoring the condition of the core in steel cord conveyor belts–Tests of the measuring probe and the design of the DiagBelt system. Measurement 123, 4853.CrossRefGoogle Scholar
Chen, WH, Hsu, HJ and Lin, YC (2022) Implementation of a real-time uneven pavement detection system on FPGA platforms. In 2022 IEEE International Conference on Consumer Electronics-Taiwan. IEEE, 587588.CrossRefGoogle Scholar
Ganesh, P, Chen, Y, Yang, Y, et al. (2022) YOLO-ReT: Towards high accuracy real-time object detection on edge GPUs. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 32673277.CrossRefGoogle Scholar
Girshick, R (2015) Fast R-CNN [EB/OL]. Available at https://arxiv.org/abs/1504.08083 (accessed 15 September 2021).Google Scholar
Guo, X, Liu, X, Królczyk, G, et al. (2022) Damage detection for conveyor belt surface based on conditional cycle generative adversarial network. Sensors 22, 3485.CrossRefGoogle ScholarPubMed
Guo, X, Liu, X, Zhou, H, et al. (2022) Belt tear detection for coal mining conveyors. Micromachines 13, 449.CrossRefGoogle ScholarPubMed
He, K, Gkioxari, G, Dollar, P, et al. (2018) Mask R-CNN [EB/OL]. Available at https://arxiv.org/abs/1703.06870 (accessed 15 September 2021).Google Scholar
Howard, AG, Zhu, M, Chen, B, et al. (2017) MobileNets: Efficient convolutional neural networks for mobile vision applications [EB/OL]. Available at https://arxiv.org/abs/1704.04861 (accessed 18 September 2021).Google Scholar
Jaderberg, M, Simonyan, K and Zisserman, A (2015) Spatial transformer networks. In Advances in Neural Information Processing Systems, 28.Google Scholar
Ji, J, Miao, C and Li, X (2020) Research on the energy-saving control strategy of a belt conveyor with variable belt speed based on the material flow rate. Plos one 15, e0227992.CrossRefGoogle ScholarPubMed
Koonce, B (2021) EfficientNet. In Koonce B (ed), Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization. Berkeley: Apress, 109123.CrossRefGoogle Scholar
Lavin, A and Gray, S. (2016) Fast algorithms for convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 40134021.CrossRefGoogle Scholar
Li, X, Huang, H, Chen, T, et al. (2022) A hardware-efficient computing engine for FPGA-based deep convolutional neural network accelerator. Microelectronics Journal 128, 105547.CrossRefGoogle Scholar
Li, W, Li, C and Yan, F (2021) Research on belt tear detection algorithm based on multiple sets of laser line assistance. Measurement 174, 109047.CrossRefGoogle Scholar
Li, S, Wang, Q, Jiang, J, et al. (2022) An efficient CNN accelerator using inter-frame data reuse of videos on FPGAs. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 30(11), 15871600.CrossRefGoogle Scholar
Liu, W, Anguelov, D, Erhan, D, et al. (2016) SSD: Single shot multibox detector. In Proceedings of the Computer Vision–ECCV 2016: 14th European Conference, 11–14 October, Amsterdam, The Netherlands, Part I 14. Springer International Publishing, 21–37.CrossRefGoogle Scholar
Liu, Z, Li, J, Shen, Z, et al. (2017) Learning efficient convolutional networks through network slimming. In Proceedings of the IEEE International Conference on Computer Vision. 27362744.CrossRefGoogle Scholar
Liu, M, Zhu, Q, Yin, Y, et al. (2022) Damage Detection Method of Mining Conveyor Belt Based on Deep Learning. IEEE Sensors Journal 22, 1087010879.CrossRefGoogle Scholar
Lu, L, Liang, Y, Xiao, Q, et al. (2017) Evaluating fast algorithms for convolutional neural networks on FPGAs. In 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE, 101108.CrossRefGoogle Scholar
Ma, Y, Cao, Y, Vrudhula, S, et al. (2018) Optimizing the convolution operation to accelerate deep neural networks on FPGA. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 26(7), 13541367.CrossRefGoogle Scholar
Nguyen, DT, Nguyen, TN, Kim, H, et al. (2019) A high-throughput and power-efficient FPGA implementation of YOLO CNN for object detection. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 27(8), 18611873.CrossRefGoogle Scholar
Qiu, J, Wang, J, Yao, S, et al. (2016) Going deeper with embedded fpga platform for convolutional neural network. In Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. 26–35.CrossRefGoogle Scholar
Qu, D, Qiao, T, Pang, Y, et al. (2020) Research on ADCN method for damage detection of mining conveyor belt. IEEE Sensors Journal 21, 86628669.CrossRefGoogle Scholar
Redmon, J, Divvala, S, Girshick, R, et al. (2016) You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 779788.CrossRefGoogle Scholar
Ribeiro, RG, Júnior, JRC, Cota, LP, et al. (2019) Unmanned aerial vehicle location routing problem with charging stations for belt conveyor inspection system in the mining industry. IEEE Transactions on Intelligent Transportation Systems 21, 41864195.CrossRefGoogle Scholar
Salim, O, Dey, S, Masoumi, H, et al. (2021) Crack monitoring system for soft rock mining conveyor belt using UHF RFID sensors. IEEE Transactions on Instrumentation and Measurement 70, 112.CrossRefGoogle Scholar
Terven, J and Cordova-Esparza, D (2023) A comprehensive review of YOLO: From YOLOv1 and beyond. arXiv preprint arXiv:2304.00501.Google Scholar
Trybała, P, Blachowski, J, Błażej, R, et al. (2020) Damage detection based on 3d point cloud data processing from laser scanning of conveyor belt surface. Remote Sensing 13, 55.CrossRefGoogle Scholar
Wang, Y, Wang, Y, Dang, L (2020) Video detection of foreign objects on the surface of belt conveyor underground coal mine based on improved SSD. Journal of Ambient Intelligence and Humanized Computing 110.Google Scholar
Wang, Q, Wu, B, Zhu, P, et al. (2020) ECA-Net: Efficient channel attention for deep convolutional neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 1153411542.CrossRefGoogle Scholar
Xianguo, L, Lifang, S, Zixu, M, et al. (2018) Laser-based on-line machine vision detection for longitudinal rip of conveyor belt. Optik 168, 360369.CrossRefGoogle Scholar
Xu, S, Zhou, Y, Huang, Y, et al. (2022) YOLOv4-tiny-based coal gangue image recognition and FPGA implementation. Micromachines 13(11), 1983.CrossRefGoogle ScholarPubMed
Yang, R, Qiao, T, Pang, Y, et al. (2020) Infrared spectrum analysis method for detection and early warning of longitudinal tear of mine conveyor belt. Measurement 165, 107856.CrossRefGoogle Scholar
Yu, Z and Bouganis, CS (2020) A parameterisable FPGA-tailored architecture for YOLOv3-tiny. In Rincón F, Barba J, So H, Diniz P, and Caba J (eds), Applied Reconfigurable Computing. Architectures, Tools, and Applications: 16th International Symposium, ARC 2020. Cham: Springer International Publishing, 330344.Google Scholar
Yu, L, Zhu, J, Zhao, Q, et al. (2022) An efficient YOLO algorithm with an attention mechanism for vision-based defect inspection deployed on FPGA. Micromachines 13(7), 1058.CrossRefGoogle Scholar
Zhang, M, Cao, Y, Jiang, K, et al. (2022) Proactive measures to prevent conveyor belt failures: Deep learning-based faster foreign object detection. Engineering Failure Analysis 141, 106653.CrossRefGoogle Scholar
Zhang, F, Li, Y and Ye, Z (2022) Apply Yolov4-tiny on an FPGA-based accelerator of convolutional neural network for object detection. Journal of Physics: Conference Series 2303, 012032.CrossRefGoogle Scholar
Zhang, M, Shi, H, Zhang, Y, et al. (2021) Deep learning-based damage detection of mining conveyor belt. Measurement 175, 109130.CrossRefGoogle Scholar
Zhang, M, Zhang, Y, Zhou, M, et al. (2021) Application of lightweight convolutional neural network for damage detection of conveyor belt. Applied sciences 11, 7282.CrossRefGoogle Scholar