基于E-FCNN的电力巡检图像增强

doi:10.11930/j.issn.1004-9649.202004144

中国电力 ›› 2021, Vol. 54 ›› Issue (5): 179-185.DOI: 10.11930/j.issn.1004-9649.202004144

基于E-FCNN的电力巡检图像增强

白万荣¹, 张驯¹, 朱小琴¹, 刘吉祥¹, 程其玉², 赵琰², 邵洁²

1. 国网甘肃省电力公司电力科学研究院，甘肃兰州 730050;
2. 上海电力大学电子与信息工程学院, 上海 200090

收稿日期:2020-04-17 修回日期:2020-12-16 发布日期:2021-05-05
作者简介:白万荣(1985-),男,硕士,高级工程师,从事电力网络与信息安全研究,E-mail:baiwanrong@yeah.net;邵洁(1981-),女,通信作者,副教授,从事电力系统智能化处理、机器视觉分析研究,E-mail:shaojie@shiep.edu.cn
基金资助:
国家自然科学基金资助项目(基于显著特征和数据压缩的图像摘要关键技术研究，F020603)

E-FCNN Based Electric Power Inspection Image Enhancement

BAI Wanrong¹, ZHANG Xun¹, ZHU Xiaoqin¹, LIU Jixiang¹, CHENG Qiyu², ZHAO Yan², SHAO Jie²

1. Gansu Electric Power Research Institute, Lanzhou 730050, China;
2. Department of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Received:2020-04-17 Revised:2020-12-16 Published:2021-05-05
Supported by:
This work is supported by National Natural Science Foundation of China (Study of Key Techniques of Image Hashing Based on Salient Feature and Data Compression, No.F020603)

摘要/Abstract

摘要： 为了解决无人机巡线、无人值守变电站机器人巡检中，由于距离过远或机器抖动造成的采集图像待检目标分辨率低、图像模糊等问题，提出一种边缘感知反馈卷积神经网络E-FCNN。该网络在传统超分辨率网络基础上增加了残差模块和反馈机制，实现细节特征的提取和强化，并通过边缘感知分支补充纹理信息，提升了图像的细节描述。通过测试集实验结果表明：提出的边缘感知反馈卷积神经网络无论在主观视觉质量，或是峰值信噪比等客观评价指标上，都明显优于其他相关算法。且在基于无人机巡检的绝缘子检测应用中能够有效提高绝缘子检测率。

关键词: 电力巡检, 超分辨率, 图像增强, 卷积神经网络, 边缘感知

Abstract: For UAV patrol of transmission lines and robot inspection of unattended substations, low image resolution is one of the main problems due to long shooting distance or machine shaking. In order to solve this problem, we propose an edge-aware feedback convolutional neural network (E-FCNN), which not only adds Resnet blocks and feedback mechanism to the conventional super-resolution network to strengthen the ability of feature extraction, but also adds texture information to the edge-aware branch to enhance the image detail. Extensive experiments show that the proposed algorithm is superior to other existing algorithms, both in subjective visual quality and objective evaluation indexes such as peak signal-to-noise ratio. Practically, the proposed algorithm can improve the accuracy of insulator detection in UAV transmission line inspection.

Key words: electric power inspection, super-resolution, image enhancement, convolutional neural network, edge aware

白万荣, 张驯, 朱小琴, 刘吉祥, 程其玉, 赵琰, 邵洁. 基于E-FCNN的电力巡检图像增强[J]. 中国电力, 2021, 54(5): 179-185.

BAI Wanrong, ZHANG Xun, ZHU Xiaoqin, LIU Jixiang, CHENG Qiyu, ZHAO Yan, SHAO Jie. E-FCNN Based Electric Power Inspection Image Enhancement[J]. Electric Power, 2021, 54(5): 179-185.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202004144

https://www.electricpower.com.cn/CN/Y2021/V54/I5/179

参考文献

[1] 仝卫国, 苑津莎, 李宝树. 图像处理技术在直升机巡检输电线路中的应用综述[J]. 电网技术, 2010, 34(12): 204-208
TONG Weiguo, YUAN Jinsha, LI Baoshu. Application of image processing in patrol inspection of overhead transmission line by helicopter[J]. Power System Technology, 2010, 34(12): 204-208
[2] 李道明. 可配置变电站室内图像巡检系统及关键算法研究[D]. 杭州: 浙江大学, 2019.
Li Daoming, Research on configurable image processing system for interior substation inspection and the key algorithms[D]. Hangzhou: Zhejiang University, 2019.
[3] 魏立明, 杨天野. 基于图像增强和边缘检测的电力设备故障诊断研究[J]. 吉林建筑工程学院学报, 2012, 29(4): 84-86
WEI Liming, YANG Tianye. Research on fault diagnosis of electrical equipment based on image enhancement and edge detection[J]. Journal of Jilin Institute of Architecture & Civil Engineering, 2012, 29(4): 84-86
[4] 孙宁宁, 张建国. 超分辨率重建技术在电力监控系统中的应用[C]//2011年中国电机工程学会年会, 2011.
SUN Ningning, ZHANG Jianguo. Power monitoring system based on super-resolution reconstruction[C]//2011 Annual Meeting of Chinese Society of Electrical Engineering, 2011.
[5] 赵雨田, 都洪基. 电力线数字灰度图像增强算法[J]. 计算机系统应用, 2017, 26(2): 104-111
ZHAO Yutian, DU Hongji. Gray image enhancement algorithm of power line[J]. Computer Systems & Applications, 2017, 26(2): 104-111
[6] 薛璐妍, 王红. CLAHE融合小波去噪的电力图像增强算法[J]. 数据通信, 2018(6): 31-33
XUE Luyan, WANG Hong. The algorithm for electric equipment image enhancement based on CLAHE fusion wavelet denoising[J]. Data Communications, 2018(6): 31-33
[7] 林国强, 王博, 孔英会, 等. 基于压缩感知的变电站巡检图像超分辨率重建[C]//第37届中国控制会议论文集. 武汉, 2018: 337-341.
[8] 陈智雨, 巩少岩, 俞学豪, 等. 基于自学习超分辨率的电力线路巡检可视化[J]. 电力信息与通信技术, 2019, 17(9): 11-16
CHEN Zhiyu, GONG Shaoyan, YU Xuehao, et al. Super-resolution visual patrol inspection for power line based on self-learning[J]. Electric Power Information and Communication Technology, 2019, 17(9): 11-16
[9] 王社军, 钱瑞琦, 韩周迎, 等. 浅谈基于图像增强智能变电站表盘识别方法[J]. 中国设备工程, 2019(10): 167-169
[10] 严利雄, 刘晓华, 李茗, 等. 基于生成对抗网络的红外图像电力设备超分辨算法研究[J]. 通信电源技术, 2020, 37(3): 40-43
YAN Lixiong, LIU Xiaohua, LI Ming, et al. Research on infrared images super-resolution algorithm on power equipment based on generative adversarial network[J]. Telecom Power Technology, 2020, 37(3): 40-43
[11] KRIZHEVSKY A, SUTSKEVER I, HINTON G. ImageNet classification with deep convolutional neural networks[J]. Advances in Neural Information Processing Systems, 2012: 1097-1105.
[12] 李文璞, 谢可, 廖逍, 等. 基于Faster RCNN变电设备红外图像缺陷识别方法[J]. 南方电网技术, 2019, 13(12): 79-84
LI Wenpu, XIE Ke, LIAO Xiao, et al. Intelligent diagnosis method of infrared image for transformer equipment based on improved faster RCNN[J]. Southern Power System Technology, 2019, 13(12): 79-84
[13] LIU B L, AIT-BOUDAOUD D. Effective image super resolution via hierarchical convolutional neural network[J]. Neurocomputing, 2020, 374: 109-116.
[14] XIE Saining, TU Zhuowen, Holistically-nested edge detection[J]. International Journal of Computer Vision, 2017, 125: 3-8.
[15] LIM Bee, SON Sanghyun, KIM Heewon, et al. Enhanced deep residual networks for single image super-resolution[C]//IEEE Computer Vision and Pattern Recognition Workshops (CVPRW), 2017.
[16] LI Zhen, YANG Jinglei, LIU Zheng, et al. Feedback network for image super-resolution[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[17] DONG Chao, CHEN Change Loy, HE Kaiming, et al. Image super-resolution using deep convolutional networks[J]. TPAMI, 2016.
[18] PELEG T, ELAD M. A statistical prediction model based on sparse representations for single image super-resolution[J]. IEEE Transactions on Image Processing, 2014, 23(6): 2569-2582.
[19] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
[20] REN Shaoqing, HE Kaiming, GIRSHICK R, et. al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Neural Information Processing Systems (NIPS), 2015.

基于E-FCNN的电力巡检图像增强

E-FCNN Based Electric Power Inspection Image Enhancement

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	吴军英, 路欣, 刘宏, 张彬, 柴守亮, 刘蕴春, 王佳楠. 基于Spearman-GCN-GRU模型的超短期多区域电力负荷预测[J]. 中国电力, 2024, 57(6): 131-140.
[2]	娄奇鹤, 李荣盛, 谭捷, 袁铁江. 基于卷积神经网络的暂稳极限功率计算[J]. 中国电力, 2024, 57(4): 211-219.
[3]	周颖, 白雪峰, 王阳, 邱敏, 孙冲, 武亚杰, 李彬. 面向虚拟电厂运营的温度敏感负荷分析与演变趋势研判[J]. 中国电力, 2024, 57(1): 9-17.
[4]	王大兴, 宁妍, 汪敬培, 徐洋, 毕峻, 周铭标, 王鹏. 构建新型电力系统背景下的微电网鲁棒简化建模[J]. 中国电力, 2024, 57(1): 148-157.
[5]	皮志勇, 朱益, 廖玄, 李振兴, 方豪, 吴沛. 基于深度学习的智能变电站通信链路故障定位方法[J]. 中国电力, 2023, 56(7): 136-145.
[6]	陆友文, 崔昊, 陈佳宁, 彭祥佳, 冯双, 刘栋. 基于RA-CNN和同步相量的风电场次/超同步振荡参数智能辨识方法[J]. 中国电力, 2023, 56(4): 46-55,67.
[7]	武小琳, 栾凌, 潘连武, 李海龙. 基于LM-CNN的输变电工程造价自动计算模型[J]. 中国电力, 2023, 56(2): 157-163.
[8]	陈子含, 滕伟, 胥学峰, 丁显, 柳亦兵. 基于图卷积网络和风速差分拟合的中长期风功率预测[J]. 中国电力, 2023, 56(10): 96-105.
[9]	冯裕祺, 李辉, 李利娟, 周彦博, 谭貌, 彭寒梅. 基于CNN-GRU的光伏电站电压轨迹预测[J]. 中国电力, 2022, 55(7): 163-171.
[10]	沙骏, 徐雨森, 刘冲冲, 冯定东, 胥峥, 臧海祥. 基于变分模态分解和分位数卷积-循环神经网络的短期风功率预测[J]. 中国电力, 2022, 55(12): 61-68.
[11]	樊江川, 于昊正, 刘慧婷, 杨丽君, 安佳坤. 基于多分支门控残差卷积神经网络的短期电力负荷预测[J]. 中国电力, 2022, 55(11): 155-162,174.
[12]	杨胡萍, 余阳, 汪超, 李向军, 胡奕涛, 饶楚楚. 基于VMD-CNN-BIGRU的电力系统短期负荷预测[J]. 中国电力, 2022, 55(10): 71-76.
[13]	曾囿钧, 肖先勇, 徐方维, 郑林. 基于CNN-BiGRU-NN模型的短期负荷预测方法[J]. 中国电力, 2021, 54(9): 17-23.
[14]	徐浩, 刘利强, 吕超. 基于迁移学习的配电网内部过电压识别方法[J]. 中国电力, 2021, 54(8): 52-59.
[15]	马静怡, 崔昊杨, 张明达, 孙益辉, 许永鹏. 基于改进Faster RCNN的小尺度入侵目标识别及定位[J]. 中国电力, 2021, 54(3): 38-44.

模态框（Modal）标题

基于E-FCNN的电力巡检图像增强

E-FCNN Based Electric Power Inspection Image Enhancement

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics