水电站智能巡检机器人技术的应用

doi:10.16180/j.cnki.issn1007-7820.2023.12.014

Abstract

Abstract:

In view of the high cost of crack and seepage detection in the traditional artificial pumped storage station and the difficulty of ensuring the detection accuracy, a set of inspection robot system based on machine vision is designed in this study. A convolutional neural network which combines cross entropy and dice cost function is constructed, and an evaluation function based on total pixel accuracy, cross parallel ratio and F1-score is established to ensure the accurate detection of common cracks. In order to verify the effectiveness of the designed robot inspection system, convolutional neural network is tested in this study, and its performance is compared with common computer vision methods and manual detection methods. The comparison results show that the neural network constructed in this study has obvious progress in detection accuracy and detection efficiency.

Key words: crack detection, CNN, cost function, computer vision, inspection robot, hydraulic system

CLC Number:

TP39

SHEN Hao,ZHAO Yifeng,LI Xiao. Application of Intelligent Inspection Robot Technology for Hydropower Station[J].Electronic Science and Technology, 2023, 36(12): 99-102.

Figures/Tables 6

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References 10

[1]	牛连强, 陈向震, 张胜男, 等. 深度连续卷积神经网络模型构建与性能分析[J]. 沈阳工业大学学报, 2016, 38(6):662-666. doi: 10.7688/j.issn.1000-1646.2016.06.12
	Niu Lianqiang, Chen Xiangzhen, Zhang Shengnan, et al. Construction and performance analysis of deep continuous convolution neural network model[J]. Journal of Shenyang University of Technology, 2016, 38(6):662-666. doi: 10.7688/j.issn.1000-1646.2016.06.12
[2]	王培雷. 基于残差网络的多标签图像检索方法研究[D]. 桂林: 桂林电子科技大学, 2019:35-60.
	Wang Peilei. Research on multi tag image retrieval method based on residual network[D]. Guilin:Guilin University of Electronic Science and Technology, 2019:35-60.
[3]	王伟栋, 李菲菲, 谢林, 等. 基于深度神经网络和哈希算法的图像检索研究[J]. 电子科技, 2018, 31(10):48-52.
	Wang Weidong, Li Feifei, Xie Lin, et al. Research on image retrieval based on depth neural network and hash algorithm[J]. Electronic Science and Technology, 2018, 31(10):48-52.
[4]	郜丽鹏, 郑辉. 基于PReLUs-Softplus非线性激励函数的卷积神经网络[J]. 沈阳工业大学学报, 2018, 40(1):54-59. doi: 10.7688/j.issn.1000-1646.2018.01.10
	Gao Lipeng, Zheng Hui. Convolution neural network based on prelus softplus nonlinear excitation function[J]. Journal of Shenyang University of Technology, 2018, 40(1):54-59. doi: 10.7688/j.issn.1000-1646.2018.01.10
[5]	梁珊, 刘晓波. 基于交叉熵代价函数的DBN转子系统故障识别分析[J]. 南昌航空大学学报:自然科学版, 2018, 32(3):1-7.
	Liang Shan, Liu Xiaobo. Fault identification analysis of DBN rotor system based on cross entropy cost function[J]. Journal of Nanchang University of Aeronautics and Technology (Natural Science Edition), 2018, 32(3):1-7.
[6]	刘昊, 王冠华, 章强, 等. 3D脑肿瘤分割的Dice损失函数的优化[J]. 中国医疗设备, 2019, 34(5):20-23,31.
	Liu Hao, Wang Guanhua, Zhang Qiang, et al. Optimization of dice loss function in 3D brain tumor segmentation[J]. Chinese Medical Equipment, 2019, 34(5):20-23,31.
[7]	李海涛, 戴莉莉, 顾海燕, 等. 样本尺寸对遥感影像FCN训练模型的影响分析[J]. 测绘科学, 2019, 44(6):133-137.
	Li Haitao, Dai Lili, Gu Haiyan, et al. Analysis of the influence of sample size on FCN training model of remote sensing image[J]. Surveying and Mapping Science, 2019, 44(6):133-137.
[8]	盛恒, 黄铭, 杨晶晶. 基于FasterR-CNN和IoU优化的实验室人数统计与管理系统[J]. 计算机应用, 2019, 39(6):1669-1674. doi: 10.11772/j.issn.1001-9081.2018102182
	Sheng Heng, Huang Ming, Yang Jingjing. Laboratory population statistics and management system optimized by FasterR-CNN and IoU[J]. Computer Application, 2019, 39(6):1669-1674. doi: 10.11772/j.issn.1001-9081.2018102182
[9]	黄浩, 徐海华, 王羡慧, 等. 自动发音错误检测中基于最大化F1值准则的区分性特征补偿训练算法[J]. 电子学报, 2015, 43(7):1294-1299.
	Huang Hao, Xu Haihua, Wang Xianhui, et al. Training algorithm of differential feature compensation based on maximizing F1 value in automatic pronunciation error detection[J]. Journal of Electronics, 2015, 43(7):1294-1299.
[10]	黄浩, 王建明, 哈力旦·阿布都热依木, 等. 自动发音错误检测中基于F1值最大化的声学模型训练方法[J]. 声学学报, 2013, 38(6):751-758.
	Huang Hao, Wang Jianming, Khalidin Abdureyimu, et al. Acoustic model training method based on F1 value maximization in automatic pronunciation error detection[J]. Journal of Acoustics, 2013, 38(6):751-758.

	CNN	Scharr	Snake	图搜索
检测时间/ms	320	480	470	590
误检率/%	0.2	3.2	6.2	2.0
准确性/%	98	87	91	95

拍摄距离 /mm	人工测量宽度/mm	CNN测量宽度/mm	绝对误差 /mm	相对误差 /%
1 403	0.35	0.34	-0.01	-2.8
1 076	0.36	0.37	0.01	-2.7
2 012	0.78	0.77	-0.01	-1.2
2 858	0.89	0.89	0.00	0.0
2 299	0.78	0.80	0.02	2.6
2 954	0.67	0.70	0.03	4.4
1 076	0.56	0.54	-0.02	-3.6
1 298	0.78	0.76	-0.02	-2.6

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Application of Intelligent Inspection Robot Technology for Hydropower Station

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