| 陈庆,闫斌,叶润,周小佳.航拍绝缘子卷积神经网络检测及自爆识别研究[J].电子测量与仪器学报,2017,31(6):942-953 |
| 航拍绝缘子卷积神经网络检测及自爆识别研究 |
| Insulator detection and recognition of explosion fault based on convolutional neural networks |
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| DOI：10.13382/j.jemi.2017.06.018 |
| 中文关键词: 卷积神经网络 绝缘子 自爆 检测 识别 |
| 英文关键词:convolutional neural networks insulator explosion detection recognition |
| 基金项目: |
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| Author | Institution |
| Chen Qing | College of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China |
| Yan Bin | College of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China |
| Ye Run | College of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China |
| Zhou Xiaojia | College of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China |
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| 中文摘要: |
| 无人机巡检通过搭载的高清相机和图传设备可获取大量详实的巡检影像。绝缘子是输电线路中极其重要且用量庞大的部件，在图像视频中快速准确地检测出绝缘子可为无人机贴近铁塔和输电线路进行细节巡视的测距和避障飞行提供可靠的依据；同时绝缘子为故障多发元件严重威胁电网的安全，需充分利计算机技术对其进行故障诊断。通过搭建卷积神经网络，在由5个卷积池化模块和2个全连接模块组成的经典架构的基础上，对网络进行改进，实现在复杂航拍背景中绝缘子检测。同时在训练的网络模型中抽取绝缘子的特征融入自组织特征映射网络中实现显著性检测，结合超像素分割和轮廓检测等图像处理方法对绝缘子进行数学建模，提出一种针对绝缘子自爆故障的识别算法，取代人工分析，降低由人为经验判断可能造成的误差。经测试，复杂航拍背景下的绝缘子检测精度达90%以上，自爆识别准确率达到85%以上，均满足工程需求，有效提升巡检的效率和智能化水平。 |
| 英文摘要: |
| Unmanned aerial vehicles (UAVs) equipped with HD camera can obtain a large number of detailed inspection images of the insulators which are indispensable components in the transmission lines. A quick and accurate detection of the insulator can provide a reliable basis for distance measurement and obstacle avoidance for UAV when flying close to towers for details. Simultaneously, as a fault prone component, the insulators seriously threat the network security, thus computer technology is required for fault diagnosis. The detection of the insulator image with the complex aerial background is implemented by constructing a convolutional neural network (CNN), which has the classic architecture of five modules of convolution and pooling, two modules of fully connected layers. In this paper, a recognition algorithm for explosion fault based on saliency detection is proposed, which uses the trained network model to extract the features. Then we put the saliency maps into a SOM network and build the mathematical module via super pixel segmentation, contour detection, and other image processing methods. The test shows that the algorithm can reduce the errors caused by manual analysis. The test also demonstrates that the detection rate of the insulator can reach 90% and the recognition rate of explosion fault can reach 85% with complex background. They effectively improve the efficiency of inspection and make the inspection more intelligent. |
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