Abstract
In this paper, a system is proposed for extracting insulators on power transmission lines from photographs captured by an unmanned aerial vehicle. The approximate regions of the insulators are first determined by a support vector machine with the histogram of oriented gradients as the feature descriptor. Then, the specific regions of insulators are detected and extracted by the GrabCut algorithm. In advance, some constraint conditions, such as the value ranges of color component values as well as the relationships between the component values in three color models, need to be specified. In our system, an interactive interface is developed to help determine these conditions. According to the experimental results, our system is capable of removing most of the backgrounds and extracting the insulators from photographs.
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Abdulla W (2017) Mask r-cnn for object detection and instance segmentation on keras and tensorflow. https://github.com/matterport/Mask_RCNN
Carpineto C, Michini C, Nicolussi R (2009) A concept lattice-based kernel for SVM text classification. In: International conference on formal concept analysis. Springer, pp 237–250
Chapelle O, Haffner P, Vapnik VN (1999) Support vector machines for histogram-based image classification. IEEE Trans Neural Netw 10(5):1055–1064
Chuang J, Weiskopf D, Moller T (2009) Hue-preserving color blending. IEEE Trans Vis Comput Graph 15(6):1275–1282
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005., vol 1. IEEE, pp 886–893
Foody GM, Mathur A (2004) A relative evaluation of multiclass image classification by support vector machines. IEEE Trans Geosci Remote Sens 42(6):1335–1343
Fuller A, Zawadzki R, Choi S, Wiley D, Werner J, Hamann B (2007) Segmentation of three-dimensional retinal image data. IEEE Trans Vis Comput Graph 13(6):1719–1726
Goertzel B, Venuto J (2006) Accurate SVM text classification for highly skewed data using threshold tuning and query-expansion-based feature selection. In: International joint conference on neural networks. IEEE, pp 1220–1225
He SY, Wang L, Xia Y, Tang YD (2013) Insulator recognition based on moments invariant features and cascade AdaBoost classifier. Appl Mech Mater 433:362–367
He K, Gkioxari G, Dollár P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969
Ibraheem NA, Hasan MM, Khan RZ, Mishra PK (2012) Understanding color models: a review. ARPN J Sci Technol 2(3):265–275
Jain DK, Dubey SB, Choubey RK, Sinhal A, Arjaria SK, Jain A, Wang H (2018) An approach for hyperspectral image classification by optimizing SVM using self organizing map. J Comput Sci 25:252–259
Joachims T (1998) Text categorization with support vector machines: Learning with many relevant features. In: European conference on machine learning. Springer, pp 137–142
Jones MJ, Rehg JM (2002) Statistical color models with application to skin detection. Int J Comput Vis 46(1):81–96
Kashuk S, Iskander M (2015) Evaluation of color space information for visualization of contamination plumes. J Vis 18(1):121–130
Leopold E, Kindermann J (2002) Text categorization with support vector machines. how to represent texts in input space? Mach Learn 46(1–3):423–444
Li W, Ye G, Huang F, Wang S, Chang W (2010) Recognition of insulator based on developed mpeg-7 texture feature. In: 3rd international congress on image and signal processing (CISP), vol 1. IEEE, pp 265–268
Li B, Wu D, Cong Y, Xia Y, Tang Y (2012) A method of insulator detection from video sequence. In: International symposium on information science and engineering (ISISE). IEEE, pp 386–389
Liao S, An J (2015) A robust insulator detection algorithm based on local features and spatial orders for aerial images. IEEE Geosci Remote Sens Lett 12(5):963–967
Lin L, Li BF, Wang L, Cong Y, Tang YD (2013) Faulty insulator diagnosis for UAV videos based on repetitiveness feature. Appl Mech Mater 423:2536–2542
Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision. Springer, pp 740–755
Liu Y, Yong J, Liu L, Zhao J, Li Z (2016) The method of insulator recognition based on deep learning. In: 4th International conference on applied robotics for the power industry (CARPI). IEEE, pp 1–5
Lu L, Pei-liang Y, Wei-wei S, Jian-wei M (2017) Similar handwritten chinese character recognition based on CNN-SVM. In: Proceedings of the international conference on graphics and signal processing. ACM, pp 16–20
Naik VA, Desai AA (2017) Online handwritten Gujarati character recognition using SVM, MLP, and K-NN. In: 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, pp 1–6
Nanthagopal AP, Rajamony RS (2012) Automatic classification of brain computed tomography images using wavelet-based statistical texture features. J Vis 15(4):363–372
Nasien D, Haron H, Yuhaniz SS (2010) Support vector machine (SVM) for English handwritten character recognition. In: Second international conference on computer engineering and applications (ICCEA), vol 1. IEEE, pp 249–252
Pérez P, Hue C, Vermaak J, Gangnet M (2002) Color-based probabilistic tracking. In: European conference on computer vision. Springer, pp 661–675
Rother C, Kolmogorov V, Blake A (2004) Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans Graph 23(3):309–314
Shaik KB, Ganesan P, Kalist V, Sathish B, Jenitha JMM (2015) Comparative study of skin color detection and segmentation in HSV and YCbCr color space. Procedia Comput Sci 57:41–48
Shanthi N, Duraiswamy K (2010) A novel SVM-based handwritten tamil character recognition system. Pattern Anal Appl 13(2):173–180
Tan C, Hong T, Chang T, Shneier M (2006) Color model-based real-time learning for road following. In: Intelligent transportation systems conference 2006, ITSC’06. IEEE pp 939–944
Wang X, Zhang Y (2016) Insulator identification from aerial images using support vector machine with background suppression. In: International conference on unmanned aircraft systems (ICUAS). IEEE, pp 892–897
Zarit BD, Super BJ, Quek FK (1999) Comparison of five color models in skin pixel classification. In: International workshop on recognition, analysis, and tracking of faces and gestures in real-time systems. IEEE, pp 58–63
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The author (Chi Zhang) appreciates the financial support of China Scholarship Council during his study at Kyoto University.
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Zhang, C., Gong, Qw., Wang, T. et al. Visual extraction system for insulators on power transmission lines from UAV photographs using support vector machine and color models. J Vis 23, 1101–1112 (2020). https://doi.org/10.1007/s12650-020-00672-9
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DOI: https://doi.org/10.1007/s12650-020-00672-9