一种可迭代基于多向自相关的航拍电力线图像增强方法

doi:10.13973/j.cnki.robot.2015.0738

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曹蔚然, 朱琳琳, 韩建达. 一种可迭代基于多向自相关的航拍电力线图像增强方法[J]. 机器人, 2015, 37(6): 738-747.DOI: 10.13973/j.cnki.robot.2015.0738. CAO Weiran, ZHU Linlin, HAN Jianda. An Iterable Multidirectional Autocorrelation Approach forAerial Power Line Image Enhancement. ROBOT, 2015, 37(6): 738-747. DOI: 10.13973/j.cnki.robot.2015.0738.

摘要

针对无人机航拍电力线图像环境背景复杂、电力线目标细弱导致目标识别率低的问题，提出了一种可迭代运行的多向自相关（iterable multidirectional autocorrelation，IMA）增强方法．该方法根据航拍图像中电力线目标的局部纵向及横向灰度分布特征设计有效的滤波模板，用方向滤波的结果进行自相关增强．同时，这种自我增强可以多次迭代运行以达到满意的图像增强效果．通过一系列实验将Canny、Hessian与IMA方法的增强结果进行对比，实验结果显示，所提出的IMA方法比Canny和Hessian方法更适于无人机航拍电力线图像的增强操作．IMA方法不但运算速度快，而且能在大幅减弱航拍图像中复杂环境背景的同时增强电力线目标，从而有效提高图像的电力线目标检测识别率．

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关键词 ：航拍电力线图像, 可迭代多向自相关, 图像增强, 灰度分布, 滤波

Abstract：

A power line image photographed by UAV(unmanned aerial vehicle) has usually a complex background, wherein the thin power lines are so weak that the target lines detection rate is low. To solve this problem, an iterable multidirectional autocorrelation(IMA) approach is proposed to enhance image. Firstly, an effective filtering template is designed according to the local grey level distribution along longitudinal and lateral directions of a power line in a UAV aerial image, and the results of the directional filtering are used to perform an autocorrelational enhancement. The autocorrelational enhancement can be performed iteratively to get a satisfactory image enhancement result. Image enhancement results of IMA are compared with those of Canny, Hessian approaches in a series of experiments. Experiments results show that the proposed IMA approach is more suitable for UAV aerial image enhancement than Canny and Hessian approaches. The IMA approach is fast, and it can weaken complex background in aerial image dramatically while enhancing power line targets, which effectively improves recognition rate of power line targets in images.

Key words： aerial power line image iterable multidirectional autocorrelation(IMA) image enhancement grey level distribution filtering

收稿日期: 2015-04-28 录用日期: 2015-09-06

TP751.1

基金资助:

国家自然科学基金（61473282，61203340，61305121）

通讯作者: 曹蔚然，caoweiran@sia.cn E-mail: caoweiran@sia.cn

作者简介: 陈国兴（1990-），男，硕士生．研究领域：模式识别，故障检测．
耿艳利（1982-），女，博士，讲师．研究领域：康复辅具，模式识别．
刘作军（1971-），男，博士，教授．研究领域：康复辅具，智能机器人，模式识别．

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.2015.0738 或 https://robot.sia.cn/CN/Y2015/V37/I6/738

[1] Golightly I,Jones D.Visual control of an unmanned aerial vehicle for power line inspection[C]//12th International Conference on Advanced Robotics.Piscataway,USA:IEEE,2005:288-295.

[2] Yan G J,Li C Y,Zhou G Q,et al.Automatic extraction of power lines from aerial images[J].IEEE Geoscience and Remote Sensing Letters,2007,4(3):387-391.

[3] Li Z R,Liu Y,Hayward R,et al.Knowledge-based power line detection for UAV surveillance and inspection systems[C]//IEEE23rd International Conference on Image and Vision Computing.Piscataway,USA:IEEE,2008:1-6.

[4] 薛岚.基于GIS的直升机输电线路巡检系统的研究与设计[D].哈尔滨:哈尔滨理工大学,2008.Xue L.Research and design of patrol inspection system of transmission lines with helicopter based on GIS[D].Harbin:Harbin University of Science and Technology,2008.

[5] 赵利坡,范慧杰,朱琳琳,等.面向巡线无人机高压线实时检测与识别算法[J].小型微型计算机系统,2012,33(4):882-886.Zhao L P,Fan H J,Zhu L L,et al.Research on real-time detection and recognition algorithm of high-voltage transmission line for inspection with unmanned aerial vehicle[J].Journal of Chinese Computer Systems,2012,33(4):882-886.

[6] Cao W R,Zhu L L,Han J D,et al.High voltage transmission line detection for UAV based routing inspection[C]//IEEE/ASME International Conference on Advanced Intelligent Mechatronics.Piscataway,USA:IEEE,2013:554-558.

[7] Zhu L L,Cao W R,Han J D,et al.A double-side filter based power line recognition method for UAV vision system[C]//IEEE International Conference on Robotics and Biomimetics.Piscataway,USA:IEEE,2013:2655-2660.

[8] Frangi A,Niessen W,Vincken K,et al.Multiscale vessel enhancement filtering[M]//Wells W,Colchester A,Delp S.Medical Image Computing and Computer-Assisted Intervention.Berlin,Germany:Springer-Verlag,1998:130-137.

[9] 李光明,田捷,赵明昌,等.基于Hessian矩阵的中心路径提取算法[J].软件学报,2003,14(12):2074-2081.Li G M,Tian J,Zhao M C,et al.Centerline extraction based on Hessian matrix[J].Journal of Software,2003,14(12):2074-2081.

[10] 许燕,胡广书,商丽华,等.基于Hessian矩阵的冠状动脉中心线的跟踪算法[J].清华大学学报:自然科学版,2007,47(6):889-892.Xu Y,Hu G S,Shang L H,et al.Adaptive tracking extraction of vessel centerlines in coronary arteriograms using Hessian matrix[J].Journal of Tsinghua University:Science and Technology,2007,47(6):889-892.

[11] Mirhassani S M,Hosseini M M,Behrad A.Improvement of Hessian based vessel segmentation using two stage threshold and morphological image recovering[C]//International Conference on Innovations in Information Technology.Piscataway,USA:IEEE,2009:50-54.

[12] Drechsler K,Laura C O.Comparison of vesselness functions for multiscale analysis of the liver vasculature[C]//IEEE International Conference on Information Technology and Applications in Biomedicine.Piscataway,USA:IEEE,2010:1-5.

[13] Sherlock B G,Monro D M,Millard K.Fingerprint enhancement by directional Fourier filtering[J].IEE Proceedings on Vision Image and Signal Processing,1994,141(2):87-94.

[14] Sarbadhikari S N,Basak J,Pal S K,et al.Noisy fingerprints classification with directional FFT based features using MLP[J].Neural Computing&Applications,1998,7(2):180-191.

[15] Khan M A,Khan M K,Khan M A.Improved fingerprint identification using directional filter banks[C]//IEEE7th International Multi Topic Conference.Piscataway,USA:IEEE,2003:49-54.

[16] Tantachun S,Pintavirooj C,Sangworasil M,et al.Directional filter bank:An enhancement for fingerprint feature detection[C]//IEEE Conference on Industrial Electronics and Applications.Piscataway,USA:IEEE,2006:1-5.

[17] Ali M,Clausi D.Using the Canny edge detector for feature extraction and enhancement of remote sensing images[C]//Geoscience and Remote Sensing Symposium,vol.5.Piscataway,USA:IEEE,2001:2298-2300.

[18] 顾晓东,余道衡.PCNN的原理及其应用[J].电路与系统学报,2001,6(3):45-50.Gu X D,Yu D H.Theory and application of PCNN[J].Journal of Circuits and Systems,2001,6(3):45-50.

[19] 马义德,戴若兰,李廉.一种基于脉冲耦合神经网络和图像熵的自动图像分割方法[J].通信学报,2002,23(1):46-51.Ma Y D,Dai R L,Li L.Automated image segmentation using pulse coupled neural networks and image's entropy[J].Journal on Communications,2002,23(1):46-51.

[20] 苗启广,王宝树.基于局部对比度的自适应PCNN图像融合[J].计算机学报,2008,31(5):875-880.Miao Q G,Wang B S.A novel image fusion algorithm based on local contrast and adaptive PCNN[J].Chinese Journal of Computers,2008,31(5):875-880.

[21] Simoncelli E P,Farid H.Steerable wedge filters for local orientation analysis[J].IEEE Transactions on Image Processing,1996,5(9):1377-1382.

[22] Jacob M,Unser M.Design of steerable filters for feature detection using canny-like criteria[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(8):1007-1019.

[23] Fogel I,Sagi D.Gabor filters as texture discriminator[J].Biological Cybernetics,1989,61(2):103-113.

[24] Freeman W T,Adelson E H.The design and use of steerable filters[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1991,13(9):891-906.

[25] Tai S L.Image representation using2D Gabor wavelets[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(10):959-971.

[26] Canny J.A computational approach to edge detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679-698.

[27] 曹蔚然,朱琳琳,韩建达.面向旋翼无人机的高压输电线在线检测方法[J].计算机应用研究,2014,31(10):3196-3200.Cao W R,Zhu L L,Han J D.Fast line detection method applied in UAV high voltage line inspection[J].Application Research of Computers,2014,31(10):3196-3200.