基于无人机图像的输电线断股与异物缺陷检测方法
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摘要
为提高无人机(UAV)巡检输电线路的效率,提出一种基于线结构感知的输电线断股与异物缺陷的检测方法。由于无人机巡检的图像受背景纹理及光线影响较大,采用能检测线宽度的水平与垂直方向的梯度算子提取巡检图像上的线对象,进而研究感知定律中的共线性、近似性、连续性的计算,将断续线段连接成长的线段,通过长线段的平行性计算,识别出输电线路结构中显著的平行导线组。为识别导线上安装的防振锤与间隔棒连接部件,提出一种基于局部轮廓特征的形状部件识别方法。在识别出这些连接部件的基础上,对导线进行分段分析,计算分段导线的宽度变化、灰度相似度来检测导线上的断股与异物缺陷。通过对无人机巡检采集的输电线路图像的测试,验证了这种方法在复杂的背景条件下能有效地检测导线上断股与附着异物缺陷。
In order to improve the efficiency of power transmission line inspection by Unmanned Aerial Vehicle( UAV),a new method was proposed for detecting broken transmission lines and defects of foreign body based on the perception of line structure. The transmission line image acquired by UAV was easily influenced by the background texture and light, the gradient operators of horizontal and vertical direction which can be used to detect the line width were used to extract line objects in the inspection image. The study on calculation of gestalt perception of similarity, continuity and colinearity connected the intermittent wires into continuous wires. Then the parallel wire groups were further determined through the calculation of parallel relationship between wires. In order to reduce the detection error rate, spacers and stockbridge dampers of wires were recognized based on a local contour feature. Finally, the width change and gray similarity of segmented conductor wire were calculated to detect the broken part of wire and foreign object defect. The experimental results show that the proposed method can detect broken wire strand and foreign object defect efficiently under complicated backgrounds from the transmission line of UAV images.
In order to improve the efficiency of power transmission line inspection by Unmanned Aerial Vehicle( UAV),a new method was proposed for detecting broken transmission lines and defects of foreign body based on the perception of line structure. The transmission line image acquired by UAV was easily influenced by the background texture and light, the gradient operators of horizontal and vertical direction which can be used to detect the line width were used to extract line objects in the inspection image. The study on calculation of gestalt perception of similarity, continuity and colinearity connected the intermittent wires into continuous wires. Then the parallel wire groups were further determined through the calculation of parallel relationship between wires. In order to reduce the detection error rate, spacers and stockbridge dampers of wires were recognized based on a local contour feature. Finally, the width change and gray similarity of segmented conductor wire were calculated to detect the broken part of wire and foreign object defect. The experimental results show that the proposed method can detect broken wire strand and foreign object defect efficiently under complicated backgrounds from the transmission line of UAV images.
引文
[1]SUN F,YANG Z,LI Y,et al.Methods of transmission line target recognition[J].Journal of Image and Graphics,2012,17(3):349-356.(孙凤杰,杨镇澴,李媛媛,等.输电导线图像目标识别方法[J].中国图象图形学报,2012,17(3):349-356.)
[2]YAN G,LI C,ZHOU G,et al.Automatic extraction of power lines from aerial images[J].IEEE Geoscience and Remote Sensing Letters,2007,4(3):387-391.
[3]WANG Y,HAN J,CHEN F,et al.Automatic detection method of defects of power line in visual image[J].Computer Engineering and Applications,2011,47(12):180-184.(王亚萍,韩军,陈舫明,等.可见光图像中的高压线缺陷自动诊断方法[J].计算机工程与应用,2011,47(12):180-184.)
[4]ISHINO R,TSUTSUMI F.Detection system of damaged cables using video obtained from an aerial inspection of transmission lines[C]//Proceedings of the 2004 IEEE Power Engineering Society General Meeting.Piscataway:IEEE,2004,2:1857-1862.
[5]WU G,XIAO X,SHEN R.Damaged detection and fault diagnosis for high-voltage transmission line[J].Chinese Journal of Scientific Instrument,2006,27(10):1204-1208.(吴功平,肖晓晖,申瑞刚.高压输电导线的损伤检测与故障诊断[J].仪器仪表学报,2006,27(10):1204-1208.)
[6]SHAO X,YE L,LIU C,et al.Survey of research work on contour grouping[J].Journal of Image and Graphics,2011,16(6):909-918.(邵晓芳,叶灵伟,刘朝军,等.轮廓组织研究进展[J].中国图象图形学报,2011,16(6):909-918)
[7]IQBAL Q,AGGARWAL J K.Retrieval by classification of images containing large manmade objects using perceptual grouping[J].Pattern Recognition,2002,35(7):1463-1479.
[8]ELDER J H,GOLDBERG R M.Ecological statistics of Gestalt laws for the perceptual organization of contours[J].Journal of Vision,2002,2(4):324-353.
[9]FERRARI V,FEVRIER L,JURIE F,et al.Groups of adjacent contour segments for object detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(1):36-51.
[2]YAN G,LI C,ZHOU G,et al.Automatic extraction of power lines from aerial images[J].IEEE Geoscience and Remote Sensing Letters,2007,4(3):387-391.
[3]WANG Y,HAN J,CHEN F,et al.Automatic detection method of defects of power line in visual image[J].Computer Engineering and Applications,2011,47(12):180-184.(王亚萍,韩军,陈舫明,等.可见光图像中的高压线缺陷自动诊断方法[J].计算机工程与应用,2011,47(12):180-184.)
[4]ISHINO R,TSUTSUMI F.Detection system of damaged cables using video obtained from an aerial inspection of transmission lines[C]//Proceedings of the 2004 IEEE Power Engineering Society General Meeting.Piscataway:IEEE,2004,2:1857-1862.
[5]WU G,XIAO X,SHEN R.Damaged detection and fault diagnosis for high-voltage transmission line[J].Chinese Journal of Scientific Instrument,2006,27(10):1204-1208.(吴功平,肖晓晖,申瑞刚.高压输电导线的损伤检测与故障诊断[J].仪器仪表学报,2006,27(10):1204-1208.)
[6]SHAO X,YE L,LIU C,et al.Survey of research work on contour grouping[J].Journal of Image and Graphics,2011,16(6):909-918.(邵晓芳,叶灵伟,刘朝军,等.轮廓组织研究进展[J].中国图象图形学报,2011,16(6):909-918)
[7]IQBAL Q,AGGARWAL J K.Retrieval by classification of images containing large manmade objects using perceptual grouping[J].Pattern Recognition,2002,35(7):1463-1479.
[8]ELDER J H,GOLDBERG R M.Ecological statistics of Gestalt laws for the perceptual organization of contours[J].Journal of Vision,2002,2(4):324-353.
[9]FERRARI V,FEVRIER L,JURIE F,et al.Groups of adjacent contour segments for object detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(1):36-51.