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计算机系统应用:2020,29(1):250-255
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基于超像素和深度神经网络的高压输电线路环境检测
(1.国网上海市电力公司检修公司, 上海 200063;2.华东师范大学 计算机科学与软件工程学院, 上海 200062)
High-Voltage Transmission Line Environment Detection Based on Superpixel and Deep Neural Network
(1.Inspection and Maintenance Branch, State Grid Shanghai Electric Power Co. Ltd., Shanghai 200063, China;2.School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China)
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投稿时间:2019-06-11    修订日期:2019-07-12
中文摘要: 高压输电线路通道环境对高压线路的安全性影响重大,以往都是采用人工对高压输电线路通道环境进行巡检,人工检测作业危险,效率低,难度大.因此,本文提出基于超像素和深度神经网络的航拍高压输电线路环境检测的方法.首先,采用无人机对高压输电线路通道环境航拍,将视频图像进行拼接,得到通道环境的整体图像,然后使用超像素分割算法实现图像的预分割,SURF描述子具有速度快、特性鲁棒性好,因此本文采用SURF描述子提取超像素特征向量,最后采用DNN模型对提取的超像素特征进行训练,对待检测的超像素块进行分类,从而达到检测的目的.通过本算法的应用,电力部门提高了无人机巡视特高压输电通道环境的巡检效率且验证了本算法的有效性.
Abstract:The channel environment of high-voltage transmission line has a great impact on the safety of high-voltage lines. In the past, manual inspection of the channel environment was a necessary way. Nevertheless, the manual inspection is dangerous and difficult, and its efficiency is low. To solve the problem, we propose a super-pixel combined with deep neural network for high-voltage transmission line environment detection. First, we obtain the overall image of the channel environment by the splicing technique for UAV aerial photography. Then, we employ the super-pixel segmentation algorithm to preprocess the image, in which we choose the SURF descriptor to extract the superpixel features because of its rapidity and effectiveness. Finally, the deep neural network model is used for training and classification and the superpixels are classified to achieve the purpose of detection. The experimental results on real environment images show that the inspection efficiency of high-voltage transmission line channel environment is greatly improved and the proposed algorithm is effective.
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基金项目:国家自然科学基金面上项目(61773166)
引用文本:
何冰,马泰,王欣庭,王宗洋,文颖.基于超像素和深度神经网络的高压输电线路环境检测.计算机系统应用,2020,29(1):250-255
HE Bing,MA Tai,WANG Xin-Ting,WANG Zong-Yang,WEN Ying.High-Voltage Transmission Line Environment Detection Based on Superpixel and Deep Neural Network.COMPUTER SYSTEMS APPLICATIONS,2020,29(1):250-255

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