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基于铭牌文字识别的防爆设备选型故障确定方法
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Abstract:
正确识别防爆设备铭牌所包含的防爆区域信息以辨识出设备选型故障至关重要。传统的防爆设备选型故障确定方法主要是人工巡检。为解决人工巡检识别效率低且准确性不足问题,本文提出基于铭牌文字识别的防爆设备选型故障确定方法。本文通过基于深度学习的图像识别技术,识别防爆设备铭牌对应的防爆区域信息,将铭牌所包含的防爆区域信息与设备实际位置进行比较,判断防爆设备实际安装位置区域是否在正确区域。本文通过实验验证了该方法的有效性和准确性,研究结果表明,基于铭牌文字识别的防爆设备选型故障确定方法能够有效实现防爆设备选型故障确定,对保障石化装置防爆设备安全生产具有重要意义。
It is crucial to correctly identify the explosion-proof area information contained on the nameplate of explosion-proof equipment to identify equipment selection faults. The traditional method for determining faults in explosion-proof equipment selection is mainly manual inspection. To solve the problem of low efficiency and insufficient accuracy of manual inspection identification, this paper proposes a fault determination method for explosion-proof equipment selection based on name-plate text recognition. This article uses image recognition technology based on deep learning to identify the explosion-proof area information corresponding to the nameplate of explosion- proof equipment, compares the explosion-proof area information contained in the nameplate with the actual location of the equipment, and determines whether the actual installation location of the ex-plosion-proof equipment is in the correct area. This paper verified the effectiveness and accuracy of the method through experiments. The research results show that the explosion-proof equipment selection fault determination method based on nameplate text recognition can effectively deter-mine the explosion-proof equipment selection fault, which is important for ensuring the safe pro-duction of explosion-proof equipment in petrochemical plants.
[1] | 王红梅, 袁建勇. 石油化工行业中防爆电气设备的应用分析[J]. 化工管理, 2020(35): 86-87. |
[2] | 王媛. 石油化工行业防爆电气设备的安装与应用分析[J]. 电子元器件与信息技术, 2020, 4(12): 106-107. |
[3] | 田飞. 论防爆电气应用现状及其存在问题[J]. 工程管理与技术探讨, 2023, 5(2): 132-134. |
[4] | 方应军, 张乃天, 付立东, 等. 海上防爆电气设备设施现场检查与整改策略[J]. 设备监理, 2023(3): 42-44+60. |
[5] | 赵佩佩. 计算机智能化图像识别技术研究综述[J]. 电脑知识与技术, 2023, 19(21): 109-111. |
[6] | 向志威, 杨大伟, 景康, 等. 智能图像识别技术在输电线路巡检中的应用[J]. 电子技术, 2023, 52(6): 240-241. |
[7] | 段恩悦. 基于深度学习的铭牌文字检测与识别方法研究[D]: [硕士学位论文]. 济南: 山东大学, 2021. |
[8] | Zhu, Y. and Yan, W.Q. (2022) Traffic Sign Recogni-tion Based on Deep Learning. Multimedia Tools and Applications, 81, 17779-17791. https://doi.org/10.1007/s11042-022-12163-0 |
[9] | 李文杰, 张足生, 周坤晓, 等. 基于图像矫正与去噪的车牌识别算法RD-LPRNet[J]. 东莞理工学院学报, 2023, 30(5): 22-33. |
[10] | Alam, N.-A., Ahsan, M., Based, M.A., et al. (2021) Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks. Technologies, 9, 9. https://doi.org/10.3390/technologies9010009 |
[11] | Liao, M., Wan, G., Yao, C., Chen, K. and Bai, X. (2019) Real-Time Scene Text Detection with Differentiable Binarization. Proceedings of the AAAI Conference on Artificial Intelligence, Washington DC, 7-14 February 2023, 8523-8530. |
[12] | Baek, J., Kim, G., Lee, J., Park, S., Han, D., Yun, S., Rhee, P.K., et al. (2019). What Is Wrong with Scene Text Recognition Model Comparisons? Dataset and Model Analysis. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, 27 Octo-ber-2 November 2019, 4715-4723. https://doi.org/10.1109/ICCV.2019.00481 |
[13] | Chen C, Liu M-Y, Tuzel O, et al. (2017) R-CNN for Small Object Detection. Computer Vision—ACCV 2016, Taipei, 20-24 November 2016, 214-230. https://doi.org/10.1007/978-3-319-54193-8_14 |
[14] | 杨家琦. 基于图像处理技术的钢板表面缺陷检测方法研究[D]: [硕士学位论文]. 西安: 西安理工大学, 2023. |