Foreign Object Detection of Coal Mine Belt Conveyor Based on Deep Generative Model
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    Abstract:

    A foreign object detection method based on the deep generative model is proposed to accurately detect the foreign objects on the coal mine belt conveyor. First, a conventional variational auto-encoder (VAE) is used to reconstruct the image, and the presence of foreign objects in the image is detected according to the reconstruction error between the original image and the reconstructed image. Considering that the reconstructed image generated by the VAE is usually fuzzy, a generative adversarial network (GAN) is introduced to evaluate the original image and the reconstructed image for a clearer image and higher foreign object detection accuracy. Finally, the VAE is combined with the GAN to design a deep learning algorithm suitable for belt foreign object detection. The experimental results show that compared with the baseline method the proposed method has a better effect on every evaluation indexes.

    Reference
    [1] 孙健东, 张瑞新, 贾宏军, 等. 我国露天煤矿智能化发展现状及重点问题分析. 煤炭工程, 2020, 52(11): 16–22
    [2] 王建勋. 煤矿输送带传输故障实时监测技术. 工矿自动化, 2015, 41(1): 45–48
    [3] 杨清翔, 向秀华, 孟斌, 等. 一种煤矿带式输送机故障诊断方法. 工矿自动化, 2017, 43(12): 48–52
    [4] 吕志强. 复杂环境下煤矿皮带运输异物图像识别研究[硕士学位论文]. 徐州: 中国矿业大学, 2020.
    [5] Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Computation, 2006, 18(7): 1527–1554. [doi: 10.1162/neco.2006.18.7.1527
    [6] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436–444. [doi: 10.1038/nature14539
    [7] 张荣, 李伟平, 莫同. 深度学习研究综述. 信息与控制, 2018, 47(4): 385–397, 410
    [8] Wang K, Zhao YJ, Xiong QY, et al. Research on healthy anomaly detection model based on deep learning from multiple time-series physiological signals. Scientific Programming, 2016, 2016: 5642856
    [9] Ndikumana A, Tran NH, Kim DH, et al. Deep learning based caching for self-driving cars in multi-access edge computing. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(5): 2862–2877. [doi: 10.1109/TITS.2020.2976572
    [10] 韩道岐, 张钧垚, 周玉航, 等. 基于深度强化学习的股市操盘手模型研究. 计算机工程与应用, 2020, 56(21): 145–153. [doi: 10.3778/j.issn.1002-8331.1908-0254
    [11] 郑学召, 童鑫, 郭军, 等. 煤矿智能监测与预警技术研究现状与发展趋势. 工矿自动化, 2020, 46(6): 35–40
    [12] 樊红卫, 张旭辉, 曹现刚, 等. 智慧矿山背景下我国煤矿机械故障诊断研究现状与展望. 振动与冲击, 2020, 39(24): 194–204
    [13] 罗响, 袁艳斌, 王德永, 等. 煤矿视频中复杂行为识别的持续学习模型探究. 金属矿山, 2020, (10): 118–123
    [14] Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal: MIT Press, 2014: 2672–2680
    [15] 翟正利, 梁振明, 周炜, 等. 变分自编码器模型综述. 计算机工程与应用, 2019, 55(3): 1–9. [doi: 10.3778/j.issn.1002-8331.1810-0284
    [16] 胡铭菲, 左信, 刘建伟. 深度生成模型综述.自动化学报, 2022, 48(1): 40–74.
    [17] Welling M, Kingma D P. Auto-encoding variational bayes. arXiv: 1312.6114v10, 2014.
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卢学明,于在川,许升起.基于深度生成模型的煤矿运输皮带异物检测.计算机系统应用,2022,31(5):358-363

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History
  • Received:July 26,2021
  • Revised:August 20,2021
  • Online: April 11,2022
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