复杂背景下电力线自动提取算法
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摘要
为从具有复杂背景的无人机航拍图像中准确完整地提取电力线,提出了一种结合改进Ratio算子和改进Hough变换的电力线提取新方法,命名为基于比率算子聚类和霍夫变换编组的方法(ratio-basedclusteringand hough-basedgrouping,RCHG)。首先采用Ratio算子进行电力线边缘检测,在综合考虑电力线连续性和噪声抑制能力的理念下,给出了线特征检测阈值的参考范围。然后,对边缘图像进行四连通聚类分析,消除大部分背景噪声。最后,采用Hough变换提取电力线,进一步设计直线段聚类算法,对提取结果进行直线编组并进行最小二乘拟合处理,以解决电力线断裂和重叠的问题。实验结果表明,相比传统Ratio算子结合Hough变换的方法及Line Segment Detector(LSD)算法,所提方法具有更好的抗噪性能和更高的电力线提取精度,能从具有复杂背景的无人机航拍图像中准确完整地提取出电力线,有较高的工程应用价值。
In order to extract power lines from unmanned aerial vehicle(UAV) aerial images with complex background accurately and completely, we proposed a new power line extraction method combined an improved Ratio operator with improved Hough transform, named ratio-based clustering and hough-based grouping(RCHG). Firstly, the Ratio operator was used to detect the edge of the power line. Taking the compromise between the continuity of the power line and the ability of noise suppression into consideration, we put forward a reference range of the line feature detection threshold. Then, the edge image was analyzed by 4-connected clustering to eliminate most of background noise. Finally, the Hough transform was used to extract the power line. In order to solve the problem of power line rupture and overlap, we designed the straight line clustering algorithm to organize the lines into group and fit them by the least square fit method. As experimental results shown, compared with the traditional Ratio combined Hough transform method and the Line Segment Detector(LSD) method, the proposed method has better anti-noise performance and higher power line extraction accuracy, which can extract power lines accurately and completely from the UAV aerial images in complex background, and it has high engineering application value.
In order to extract power lines from unmanned aerial vehicle(UAV) aerial images with complex background accurately and completely, we proposed a new power line extraction method combined an improved Ratio operator with improved Hough transform, named ratio-based clustering and hough-based grouping(RCHG). Firstly, the Ratio operator was used to detect the edge of the power line. Taking the compromise between the continuity of the power line and the ability of noise suppression into consideration, we put forward a reference range of the line feature detection threshold. Then, the edge image was analyzed by 4-connected clustering to eliminate most of background noise. Finally, the Hough transform was used to extract the power line. In order to solve the problem of power line rupture and overlap, we designed the straight line clustering algorithm to organize the lines into group and fit them by the least square fit method. As experimental results shown, compared with the traditional Ratio combined Hough transform method and the Line Segment Detector(LSD) method, the proposed method has better anti-noise performance and higher power line extraction accuracy, which can extract power lines accurately and completely from the UAV aerial images in complex background, and it has high engineering application value.
引文
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[16]黄新波,李菊清,张烨,等.复杂环境下覆冰绝缘子识别检测技术[J].高电压技术,2017,43(3):891-899.HUANG Xinbo,LI Juqing,ZHANG Ye,et al.Recognition and detection technology of ice-covered insulators under complex environment[J].High Voltage Engineering,2017,43(3):891-899.
[17]郝艳捧,刘国特,薛艺为,等.输电线路覆冰厚度的小波分析图像识别[J].高电压技术,2014,40(2):368-373.HAO Yanpeng,LIU Guote,XUE Yiwei,et al.Wavelet image recognition of ice thickness on transmission lines[J].High Voltage Engineering,2014,40(2):368-373.
[18]宋晓宇,袁帅,郭寒冰,等.基于自适应阈值区间的广义Hough变换图形识别算法[J].仪器仪表学报,2014,35(5):1109-1117.SONG Xiaoyu,YUAN Shuai,GUO Hanbing,et al.Pattern identification algorithm with adaptive threshold interval based extended Hough transform[J].Chinese Journal of Scientific Instrument,2014,35(5):1109-1117.
[19]XU Z,SHIN B S,KLETTE R.Accurate and robust line segment extraction using minimum entropy with Hough transform[J].IEEETrans Image Process,2015,24(3):813-822.
[20]LUO Z,JIA Y.The bovine iris location method based on dynamic contour tracking and least square principle[J].International Journal of Signal Processing,Image Processing and Pattern Recognition,2016,9(7):275-284.
[21]LEE H,RHEE H,OH J H,et al.Measurement of 3-d vibrational motion by dynamic photogrammetry using least-square image matching for sub-pixel targeting to improve accuracy[J].Sensors,2016,16(3):359.
[22]GROMPONE V G R,JAKUBOWICZ J,MOREL J M,et al.LSD:a fast line segment detector with a false detection control[J].IEEETransactions on Pattern Analysis&Machine Intelligence,2010,32(4):722-732.
[2]ARAAR O,AOUF N,DIETZ J L V.Power pylon detection and monocular depth estimation from inspection UAVs[J].Industrial Robot,2015,42(3):200-213.
[3]CHEN Y,LI Y,ZHANG H,et al.Automatic power line extraction from high resolution remote sensing imagery based on an improved radon transform[J].Pattern Recognition,2016,49(C):174-186.
[4]郑顺义,谭金石,季铮,等.基于立体相机的电力线测距技术研究[J].测绘地理信息,2010,35(1):38-39.ZHENG Shunyi,TAN Jinshi,JI Zheng,et al.Power line distance measuring technology based on stereo camera[J].Journal of Geomatics,2010,35(1):38-39.
[5]LI Z,LIU Y,HAYWARD R,et al.Knowledge-based power line detection for UAV surveillance and inspection systems[C]∥Image and Vision Computing New Zealand.Auckland,New Zealand:IEEE,2008:1-6.
[6]陈驰,麦晓明,宋爽,等.机载激光点云数据中电力线自动提取方法[J].武汉大学学报(信息科学版),2015,40(12):1600-1605.CHEN Chi,MAI Xiaoming,SONG Shuang,et al.Automatic power lines extraction method from airborne lidar point cloud[J].Geomatics and Information Science of Wuhan University,2015,40(12):1600-1605.
[7]郝艳捧,蒋晓蓝,阳林,等.基于图像分割评估运行绝缘子自然覆冰程度[J].高电压技术,2017,43(1):285-292.HAO Yanpeng,JIANG Xiaolan,YANG Lin,et al.Evaluation of natural icing condition for in-service insulators based on image segmentation[J].High Voltage Engineering,2017,43(1):285-292.
[8]李杰,周志成,高嵩,等.憎水性材料表面液滴面积分析方法[J].高电压技术,2017,43(5):1518-1525.LI Jie,ZHOU Zhicheng,GAO Song,et al.Droplet area measurement method for hydrophobic state description[J].High Voltage Engineering,2017,43(5):1518-1525.
[9]张振杰,郝向阳,刘松林,等.基于Hough一维变换的直线检测算法[J].光学学报,2016,36(4):166-173.ZHANG Zhenjie,HAO Xiangyang,LIU Songlin,et al.Line detection based on Hough one-dimensional transform[J].Acta Optica Sinica,2016,36(4):166-173.
[10]GUO K Y,HOARE E G,JASTEH D,et al.Road edge recognition using the stripe Hough transform from millimeter-wave radar images[J].IEEE Transactions on Intelligent Transportation Systems,2015,16(2):825-833.
[11]仝卫国,苑津莎,李宝树,等.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,et al.Application of image processing in patrol inspection of overhead transmission line by helicopter[J].Power System Technology,2010,34(12):204-208.
[12]李朝阳,阎广建,肖志强,等.高分辨率航空影像中高压电力线的自动提取[J].中国图象图形学报,2007,12(6):1041-1047.LI Chaoyang,YAN Guangjian,XIAO Zhiqiang,et al.Automatic extraction of power lines from aerial images[J].Journal of Image and Graphics,2007,12(6):1041-1047.
[13]蔡克,杨忠,黄宵宁,等.一种新的直升机巡检系统电力线提取算法[J].计算机技术与发展,2009,19(10):113-116.CAI Ke,YANG Zhong,HUANG Xiaoning,et al.A new method for research on extraction of power lines in aerial inspection system[J].Computer Technology&Development,2009,19(10):113-116.
[14]谭金石,郭明.直升机电力巡检中电力线的自动提取[J].地理空间信息,2012,10(3):70-71.TAN Jinshi,GUO Ming.Automatic extraction of power lines in helicopter power patrol[J].Geospatial Information,2012,10(3):70-71.
[15]张少平,杨忠,黄宵宁,等.基于特征检测的航拍图像电力线提取方法[J].应用科技,2012,39(5):36-39.ZHANG Shaoping,YANG Zhong,HUANG Xiaoning,et al.Power line extraction from aerial images based on feature detection algorithm[J].Applied Science&Technology,2012,39(5):36-39.
[16]黄新波,李菊清,张烨,等.复杂环境下覆冰绝缘子识别检测技术[J].高电压技术,2017,43(3):891-899.HUANG Xinbo,LI Juqing,ZHANG Ye,et al.Recognition and detection technology of ice-covered insulators under complex environment[J].High Voltage Engineering,2017,43(3):891-899.
[17]郝艳捧,刘国特,薛艺为,等.输电线路覆冰厚度的小波分析图像识别[J].高电压技术,2014,40(2):368-373.HAO Yanpeng,LIU Guote,XUE Yiwei,et al.Wavelet image recognition of ice thickness on transmission lines[J].High Voltage Engineering,2014,40(2):368-373.
[18]宋晓宇,袁帅,郭寒冰,等.基于自适应阈值区间的广义Hough变换图形识别算法[J].仪器仪表学报,2014,35(5):1109-1117.SONG Xiaoyu,YUAN Shuai,GUO Hanbing,et al.Pattern identification algorithm with adaptive threshold interval based extended Hough transform[J].Chinese Journal of Scientific Instrument,2014,35(5):1109-1117.
[19]XU Z,SHIN B S,KLETTE R.Accurate and robust line segment extraction using minimum entropy with Hough transform[J].IEEETrans Image Process,2015,24(3):813-822.
[20]LUO Z,JIA Y.The bovine iris location method based on dynamic contour tracking and least square principle[J].International Journal of Signal Processing,Image Processing and Pattern Recognition,2016,9(7):275-284.
[21]LEE H,RHEE H,OH J H,et al.Measurement of 3-d vibrational motion by dynamic photogrammetry using least-square image matching for sub-pixel targeting to improve accuracy[J].Sensors,2016,16(3):359.
[22]GROMPONE V G R,JAKUBOWICZ J,MOREL J M,et al.LSD:a fast line segment detector with a false detection control[J].IEEETransactions on Pattern Analysis&Machine Intelligence,2010,32(4):722-732.