2019
Journal article
Open Access
Fault detection in power equipment via an unmanned aerial system using multi modal data
Jalil B., Leone G. R., Martinelli M., Moroni M., Pascali M. A., Berton A.
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Wire detection Article and Optics Unmanned Aerial Vehicles Instrumentation Biochemistry Atomic and Molecular Physics Infrared Images Image analysis Electrical and Electronic Engineering Analytical Chemistry Neural networks Object detection RGB Images
The power transmission lines are the link between the power plants and the points of consumption, through substations. Most importantly, the assessment of damaged aerial power lines and rusted conductors is of extreme importance for public safety; hence, power line and associated components must be periodically inspected to ensure a continuous supply and to identify any fault and defect. To achieve these objectives, recently Unmanned Aerial Vehicles (UAVs) have been widely used: in fact, they provide a safe way to bring sensors close to the power transmission lines and their associated components without halting the equipment during the inspection, and reducing operational cost and risk. In this work, the drone, equipped with multi-modal sensors, captures images in the visible and infrared domain and transmits them to the ground station. We used state-of-the-art computer vision methods to highlight expected faults (i.e. hot spots) or damaged components of the electrical infrastructure (i.e. damaged insulators). Infrared imaging, which is invariant to large scale and illumination changes in the real operating environment, supported the identification of faults in power transmission lines; while a neural network is adapted and trained to detect and classify insulators from an optical video stream. We demonstrate our approach on the data captured by a drone in Parma, Italy.
Source: Sensors (Basel) 19 (2019). doi:10.3390/s19133014
Publisher: Molecular Diversity Preservation International (MDPI),, Basel
1. Asiegbu, G.O.; Haidar, A.M.A.; Hawari, K. A Review of Defect Detection on Electrical Components Using Image Processing Technology. In Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012); Lecture Notes in Electrical Engineering; Springer: New Delhi, India, 2013; Volume 221.
2. Jadin, M.S.; Taib, S.; Kabir, S.; Yusof, M.A.B. Image Processing Methods for Evaluating Infrared Thermographic Image of Electrical Equipments. In Proceedings of the Progress in Electromagnetics Research Symposium Proceedings, Marrakesh, Morocco, 20-23 March 2011.
3. Xie, X.; Liu, Z.; Xu, C.; Zhang, Y. A Multiple Sensors Platform Method for Power Line Inspection Based on a Large Unmanned Helicopter. Sensors 2017, 6, 1222. [CrossRef] [PubMed]
4. Katrasnik, J.; Pernus, F.; Likar, B. A Survey of Mobile Robots for Distribution Power Line Inspection. IEEE Trans. Power Deliv. 2010, 25, 485-493. [CrossRef]
5. Qin, X.; Wu, G.; Lei, J.; Fan, F.; Ye, X.; Mei, Q. A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data. Sensors 2018, 18, 596. [CrossRef] [PubMed]
6. Li, Z.; Liu, Y.; Walker, R.; Hayward, R.; Zhang, J. Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform. Mach. Vis. Appl. 2010, 21, 677-686. [CrossRef]
7. Candamo, J.; Kasturi, R.; Goldgof, D.; Sarkar, S. Detection of thin lines using low-quality video from low-altitude aircraft in urban settings. IEEE Trans. Aerosp. Electron. Syst. 2009, 45, 937-949. [CrossRef]
8. Jalil, B.; Moroni, D.; Pascali, M.; Salvetti, O. Multimodal image analysis for power line inspection. In Proceedings of the International Conference on Pattern Recognition and Artificial Intelligence, Montreal, QC, Canada, 13-17 May 2018.
9. Mirallès, F.; Pouliot, N.; Montambault, S. State-of-the-art review of computer vision for the management of power transmission lines. In Proceedings of the 2014 3rd International Conference on Applied Robotics for the Power Industry (CARPI), Foz do Iguassu, Brazil, 14-16 October 2014; pp. 1-6.
10. Zhang, J.; Liu, L.; Wang, B.; Chen, X.; Wang, Q.; Zheng, T. High speed automatic power line detection and tracking for a UAV-based inspection. In Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering (ICICEE), Xi'an, China, 23-25 August 2012; pp. 266-269.
11. Sampedro, C.; Martinez, C.; Chauhan, A.; Campoy, P. A supervised approach to electric tower detection and classification for power line inspection. In Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China, 6-11 July 2014; pp. 1970-1977.
12. Wronkowicz, A. Vision Diagnostics of Power Transmission Lines: Approach to Recognition of Insulators. In Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015, Wroclaw, Poland, 25-27 May 2015; Springer: Cham, Switzerland, 2016; pp. 431-440.
13. Yan, T.; Yang, G.; Yu, J. Feature fusion based insulator detection for aerial inspection. In Proceedings of the 2017 36th Chinese Control Conference (CCC), Dalian, China, 26-28 July 2017; pp. 10972-10977.
14. Zhao, Z.; Liu, N. The recognition and localization of insulators adopting SURF and IFS based on correlation coefficient. Opt. Int. J. Light Electron Opt. 2014, 125, 6049-6052. [CrossRef]
15. Zhao, Z.; Liu, N.; Wang, L. Localization of multiple insulators by orientation angle detection and binary shape prior knowledge. IEEE Trans. Dielectr. Electr. Insul. 2015, 22, 3421-3428. [CrossRef]
16. Zhao, Z.; Xu, G.; Qi, Y.; Liu, N.; Zhang, T. Multi-patch deep features for power line insulator status classification from aerial images. In Proceedings of the 2016 International Joint Conference on Neural Networks, IJCNN 2016, Vancouver, BC, Canada, 24-29 July 2016; pp. 3187-3194.
17. Liu, Y.; Yong, J.; Liu, L.; Zhao, J.; Li, Z. The method of insulator recognition based on deep learning. In Proceedings of the 2016 4th International Conference on Applied Robotics for the Power Industry (CARPI), Jinan, China, 11-13 October 2016; pp. 1-5.
18. Levinshtein, A.; Stere, A.; Kutulakos, K.N.; Fleet, D.J.; Dickinson, S.J.; Siddiqi, K. TurboPixels: Fast Superpixels Using Geometric Flows. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31, 2290-2297. [CrossRef] [PubMed]
19. Zitnick, C.L.; Dollár, P. Edge Boxes: Locating Object Proposals from Edges. In Proceedings of the European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 6-12 September 2014; Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T., Eds.; Springer International Publishing: Cham, Switzerland, 2014; pp. 391-405.
20. Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In Advances in Neural Information Processing Systems 28; Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R., Eds.; Curran Associates, Inc.: Red Hook, NY, USA, 2015; pp. 91-99.
21. Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 39, 1137-1149. [CrossRef] [PubMed]
22. Uijlings, J.; van de Sande, K.; Gevers, T.; Smeulders, A. Selective Search for Object Recognition. Int. J. Comput. Vis. 2013, 104, 154-171. [CrossRef]
23. Abadi, M.; Barham, P.; Chen, J.; Chen, Z.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Irving, G.; Isard, M.; et al. TensorFlow: A System for Large-Scale Machine Learning. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA, 2-4 November 2016; Volume 16, pp. 265-283.
24. Lin, T.Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; Zitnick, C.L. Microsoft COCO: Common objects in context. In Proceedings of the European Conference on Computer Vision, Zurich, Switzerland, 6-12 September 2014; Springer: Cham, Switzerland, 2014; pp. 740-755.
25. Li, H.; Wang, B.; Li, L. Research on the infrared and visible power-equipment image fusion for inspection robots. In Proceedings of the 2010 1st International Conference on Applied Robotics for the Power Industry (CARPI), Montreal, QC, Canada, 5-7 October 2010; pp. 1-5.
26. Larrauri, J.I.; Sorrosal, G.; González, M. Automatic system for overhead power line inspection using an Unmanned Aerial Vehicle-RELIFO project. In Proceedings of the 2013 International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, GA, USA, 28-31 May 2013; pp. 244-252.
27. Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 1979, 9, 62-66. [CrossRef]
28. De Oliveira, J.H.E.; Lages, W.F. Robotized inspection of power lines with infrared vision. In Proceedings of the 2010 1st International Conference on Applied Robotics for the Power Industry (CARPI), Montreal, QC, Canada, 5-7 October 2010; pp. 1-6.
29. Liu, X.Z.; Tian, Z.; Wen, J.H.; Wu, J.M.; Zhang, Z.Y. SAR image registration based on affine invariant SIFT features. Opto-Electron. Eng. 2010, 37, 121-127. [CrossRef]
30. Kim, K. Survey on Registration Techniques of Visible and Infrared Images. IT Converg. Pract. 2015, 3, 25-35.
31. Hines, G. Multi-Image Registration for an Enhanced Vision System. Proc. SPIE 2013, 5108, 231-241.
32. Canny, J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 1986, PAMI-8, 679-698. [CrossRef]
33. Hough, P.V.C. Method and Means for Recognizing Complex Patterns. U.S. Patent 3,069,654, 18 December 1962.
Asiegbu, G.O., Haidar, A.M.A., Hawari, K.. A Review of Defect Detection on Electrical Components Using Image Processing Technology. Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012); Lecture Notes in Electrical Engineering. 2013; Volume 221
Jadin, M.S., Taib, S., Kabir, S., Yusof, M.A.B.. Image Processing Methods for Evaluating Infrared Thermographic Image of Electrical Equipments. Proceedings of the Progress in Electromagnetics Research Symposium Proceedings.
Xie, X., Liu, Z., Xu, C., Zhang, Y.. A Multiple Sensors Platform Method for Power Line Inspection Based on a Large Unmanned Helicopter. Sensors. 2017; 6
Katrasnik, J., Pernus, F., Likar, B.. A Survey of Mobile Robots for Distribution Power Line Inspection. IEEE Trans. Power Deliv.. 2010; 25: 485-493
Qin, X., Wu, G., Lei, J., Fan, F., Ye, X., Mei, Q.. A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data. Sensors. 2018; 18
Li, Z., Liu, Y., Walker, R., Hayward, R., Zhang, J.. Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform. Mach. Vis. Appl.. 2010; 21: 677-686
Candamo, J., Kasturi, R., Goldgof, D., Sarkar, S.. Detection of thin lines using low-quality video from low-altitude aircraft in urban settings. IEEE Trans. Aerosp. Electron. Syst.. 2009; 45: 937-949
Jalil, B., Moroni, D., Pascali, M., Salvetti, O.. Multimodal image analysis for power line inspection. Proceedings of the International Conference on Pattern Recognition and Artificial Intelligence.
Mirallès, F., Pouliot, N., Montambault, S.. State-of-the-art review of computer vision for the management of power transmission lines. Proceedings of the 2014 3rd International Conference on Applied Robotics for the Power Industry (CARPI). : 1-6
Zhang, J., Liu, L., Wang, B., Chen, X., Wang, Q., Zheng, T.. High speed automatic power line detection and tracking for a UAV-based inspection. Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering (ICICEE). : 266-269
Sampedro, C., Martinez, C., Chauhan, A., Campoy, P.. A supervised approach to electric tower detection and classification for power line inspection. Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN). : 1970-1977
Wronkowicz, A.. Vision Diagnostics of Power Transmission Lines: Approach to Recognition of Insulators. Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. 2016: 431-440
Yan, T., Yang, G., Yu, J.. Feature fusion based insulator detection for aerial inspection. Proceedings of the 2017 36th Chinese Control Conference (CCC). : 10972-10977
Zhao, Z., Liu, N.. The recognition and localization of insulators adopting SURF and IFS based on correlation coefficient. Opt. Int. J. Light Electron Opt.. 2014; 125: 6049-6052
Zhao, Z., Liu, N., Wang, L.. Localization of multiple insulators by orientation angle detection and binary shape prior knowledge. IEEE Trans. Dielectr. Electr. Insul.. 2015; 22: 3421-3428
Zhao, Z., Xu, G., Qi, Y., Liu, N., Zhang, T.. Multi-patch deep features for power line insulator status classification from aerial images. Proceedings of the 2016 International Joint Conference on Neural Networks, IJCNN 2016. : 3187-3194
Liu, Y., Yong, J., Liu, L., Zhao, J., Li, Z.. The method of insulator recognition based on deep learning. Proceedings of the 2016 4th International Conference on Applied Robotics for the Power Industry (CARPI). : 1-5
Levinshtein, A., Stere, A., Kutulakos, K.N., Fleet, D.J., Dickinson, S.J., Siddiqi, K.. TurboPixels: Fast Superpixels Using Geometric Flows. IEEE Trans. Pattern Anal. Mach. Intell.. 2009; 31: 2290-2297
Zitnick, C.L., Dollár, P., Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T.. Edge Boxes: Locating Object Proposals from Edges. Proceedings of the European Conference on Computer Vision, ECCV 2014. 2014: 391-405
Ren, S., He, K., Girshick, R., Sun, J., Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R.. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems 28. 2015: 91-99
Ren, S., He, K., Girshick, R., Sun, J.. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans. Pattern Anal. Mach. Intell.. 2017; 39: 1137-1149
Uijlings, J., van de Sande, K., Gevers, T., Smeulders, A.. Selective Search for Object Recognition. Int. J. Comput. Vis.. 2013; 104: 154-171
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M.. TensorFlow: A System for Large-Scale Machine Learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). ; Volume 16: 265-283
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.. Microsoft COCO: Common objects in context. Proceedings of the European Conference on Computer Vision. 2014: 740-755
Li, H., Wang, B., Li, L.. Research on the infrared and visible power-equipment image fusion for inspection robots. Proceedings of the 2010 1st International Conference on Applied Robotics for the Power Industry (CARPI). : 1-5
Larrauri, J.I., Sorrosal, G., González, M.. Automatic system for overhead power line inspection using an Unmanned Aerial Vehicle—RELIFO project. Proceedings of the 2013 International Conference on Unmanned Aircraft Systems (ICUAS). : 244-252
Otsu, N.. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern.. 1979; 9: 62-66
De Oliveira, J.H.E., Lages, W.F.. Robotized inspection of power lines with infrared vision. Proceedings of the 2010 1st International Conference on Applied Robotics for the Power Industry (CARPI). : 1-6
Liu, X.Z., Tian, Z., Wen, J.H., Wu, J.M., Zhang, Z.Y.. SAR image registration based on affine invariant SIFT features. Opto-Electron. Eng.. 2010; 37: 121-127
Kim, K.. Survey on Registration Techniques of Visible and Infrared Images. IT Converg. Pract.. 2015; 3: 25-35
Hines, G.. Multi-Image Registration for an Enhanced Vision System. Proc. SPIE. 2013; 5108: 231-241
Canny, J.. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell.. 1986; PAMI-8: 679-698
Hough, P.V.C.. Method and Means for Recognizing Complex Patterns. U.S. Patent. 1962
2. Jadin, M.S.; Taib, S.; Kabir, S.; Yusof, M.A.B. Image Processing Methods for Evaluating Infrared Thermographic Image of Electrical Equipments. In Proceedings of the Progress in Electromagnetics Research Symposium Proceedings, Marrakesh, Morocco, 20-23 March 2011.
3. Xie, X.; Liu, Z.; Xu, C.; Zhang, Y. A Multiple Sensors Platform Method for Power Line Inspection Based on a Large Unmanned Helicopter. Sensors 2017, 6, 1222. [CrossRef] [PubMed]
4. Katrasnik, J.; Pernus, F.; Likar, B. A Survey of Mobile Robots for Distribution Power Line Inspection. IEEE Trans. Power Deliv. 2010, 25, 485-493. [CrossRef]
5. Qin, X.; Wu, G.; Lei, J.; Fan, F.; Ye, X.; Mei, Q. A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data. Sensors 2018, 18, 596. [CrossRef] [PubMed]
6. Li, Z.; Liu, Y.; Walker, R.; Hayward, R.; Zhang, J. Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform. Mach. Vis. Appl. 2010, 21, 677-686. [CrossRef]
7. Candamo, J.; Kasturi, R.; Goldgof, D.; Sarkar, S. Detection of thin lines using low-quality video from low-altitude aircraft in urban settings. IEEE Trans. Aerosp. Electron. Syst. 2009, 45, 937-949. [CrossRef]
8. Jalil, B.; Moroni, D.; Pascali, M.; Salvetti, O. Multimodal image analysis for power line inspection. In Proceedings of the International Conference on Pattern Recognition and Artificial Intelligence, Montreal, QC, Canada, 13-17 May 2018.
9. Mirallès, F.; Pouliot, N.; Montambault, S. State-of-the-art review of computer vision for the management of power transmission lines. In Proceedings of the 2014 3rd International Conference on Applied Robotics for the Power Industry (CARPI), Foz do Iguassu, Brazil, 14-16 October 2014; pp. 1-6.
10. Zhang, J.; Liu, L.; Wang, B.; Chen, X.; Wang, Q.; Zheng, T. High speed automatic power line detection and tracking for a UAV-based inspection. In Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering (ICICEE), Xi'an, China, 23-25 August 2012; pp. 266-269.
11. Sampedro, C.; Martinez, C.; Chauhan, A.; Campoy, P. A supervised approach to electric tower detection and classification for power line inspection. In Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China, 6-11 July 2014; pp. 1970-1977.
12. Wronkowicz, A. Vision Diagnostics of Power Transmission Lines: Approach to Recognition of Insulators. In Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015, Wroclaw, Poland, 25-27 May 2015; Springer: Cham, Switzerland, 2016; pp. 431-440.
13. Yan, T.; Yang, G.; Yu, J. Feature fusion based insulator detection for aerial inspection. In Proceedings of the 2017 36th Chinese Control Conference (CCC), Dalian, China, 26-28 July 2017; pp. 10972-10977.
14. Zhao, Z.; Liu, N. The recognition and localization of insulators adopting SURF and IFS based on correlation coefficient. Opt. Int. J. Light Electron Opt. 2014, 125, 6049-6052. [CrossRef]
15. Zhao, Z.; Liu, N.; Wang, L. Localization of multiple insulators by orientation angle detection and binary shape prior knowledge. IEEE Trans. Dielectr. Electr. Insul. 2015, 22, 3421-3428. [CrossRef]
16. Zhao, Z.; Xu, G.; Qi, Y.; Liu, N.; Zhang, T. Multi-patch deep features for power line insulator status classification from aerial images. In Proceedings of the 2016 International Joint Conference on Neural Networks, IJCNN 2016, Vancouver, BC, Canada, 24-29 July 2016; pp. 3187-3194.
17. Liu, Y.; Yong, J.; Liu, L.; Zhao, J.; Li, Z. The method of insulator recognition based on deep learning. In Proceedings of the 2016 4th International Conference on Applied Robotics for the Power Industry (CARPI), Jinan, China, 11-13 October 2016; pp. 1-5.
18. Levinshtein, A.; Stere, A.; Kutulakos, K.N.; Fleet, D.J.; Dickinson, S.J.; Siddiqi, K. TurboPixels: Fast Superpixels Using Geometric Flows. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31, 2290-2297. [CrossRef] [PubMed]
19. Zitnick, C.L.; Dollár, P. Edge Boxes: Locating Object Proposals from Edges. In Proceedings of the European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 6-12 September 2014; Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T., Eds.; Springer International Publishing: Cham, Switzerland, 2014; pp. 391-405.
20. Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In Advances in Neural Information Processing Systems 28; Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R., Eds.; Curran Associates, Inc.: Red Hook, NY, USA, 2015; pp. 91-99.
21. Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 39, 1137-1149. [CrossRef] [PubMed]
22. Uijlings, J.; van de Sande, K.; Gevers, T.; Smeulders, A. Selective Search for Object Recognition. Int. J. Comput. Vis. 2013, 104, 154-171. [CrossRef]
23. Abadi, M.; Barham, P.; Chen, J.; Chen, Z.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Irving, G.; Isard, M.; et al. TensorFlow: A System for Large-Scale Machine Learning. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA, 2-4 November 2016; Volume 16, pp. 265-283.
24. Lin, T.Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; Zitnick, C.L. Microsoft COCO: Common objects in context. In Proceedings of the European Conference on Computer Vision, Zurich, Switzerland, 6-12 September 2014; Springer: Cham, Switzerland, 2014; pp. 740-755.
25. Li, H.; Wang, B.; Li, L. Research on the infrared and visible power-equipment image fusion for inspection robots. In Proceedings of the 2010 1st International Conference on Applied Robotics for the Power Industry (CARPI), Montreal, QC, Canada, 5-7 October 2010; pp. 1-5.
26. Larrauri, J.I.; Sorrosal, G.; González, M. Automatic system for overhead power line inspection using an Unmanned Aerial Vehicle-RELIFO project. In Proceedings of the 2013 International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, GA, USA, 28-31 May 2013; pp. 244-252.
27. Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 1979, 9, 62-66. [CrossRef]
28. De Oliveira, J.H.E.; Lages, W.F. Robotized inspection of power lines with infrared vision. In Proceedings of the 2010 1st International Conference on Applied Robotics for the Power Industry (CARPI), Montreal, QC, Canada, 5-7 October 2010; pp. 1-6.
29. Liu, X.Z.; Tian, Z.; Wen, J.H.; Wu, J.M.; Zhang, Z.Y. SAR image registration based on affine invariant SIFT features. Opto-Electron. Eng. 2010, 37, 121-127. [CrossRef]
30. Kim, K. Survey on Registration Techniques of Visible and Infrared Images. IT Converg. Pract. 2015, 3, 25-35.
31. Hines, G. Multi-Image Registration for an Enhanced Vision System. Proc. SPIE 2013, 5108, 231-241.
32. Canny, J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 1986, PAMI-8, 679-698. [CrossRef]
33. Hough, P.V.C. Method and Means for Recognizing Complex Patterns. U.S. Patent 3,069,654, 18 December 1962.
Asiegbu, G.O., Haidar, A.M.A., Hawari, K.. A Review of Defect Detection on Electrical Components Using Image Processing Technology. Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012); Lecture Notes in Electrical Engineering. 2013; Volume 221
Jadin, M.S., Taib, S., Kabir, S., Yusof, M.A.B.. Image Processing Methods for Evaluating Infrared Thermographic Image of Electrical Equipments. Proceedings of the Progress in Electromagnetics Research Symposium Proceedings.
Xie, X., Liu, Z., Xu, C., Zhang, Y.. A Multiple Sensors Platform Method for Power Line Inspection Based on a Large Unmanned Helicopter. Sensors. 2017; 6
Katrasnik, J., Pernus, F., Likar, B.. A Survey of Mobile Robots for Distribution Power Line Inspection. IEEE Trans. Power Deliv.. 2010; 25: 485-493
Qin, X., Wu, G., Lei, J., Fan, F., Ye, X., Mei, Q.. A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data. Sensors. 2018; 18
Li, Z., Liu, Y., Walker, R., Hayward, R., Zhang, J.. Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform. Mach. Vis. Appl.. 2010; 21: 677-686
Candamo, J., Kasturi, R., Goldgof, D., Sarkar, S.. Detection of thin lines using low-quality video from low-altitude aircraft in urban settings. IEEE Trans. Aerosp. Electron. Syst.. 2009; 45: 937-949
Jalil, B., Moroni, D., Pascali, M., Salvetti, O.. Multimodal image analysis for power line inspection. Proceedings of the International Conference on Pattern Recognition and Artificial Intelligence.
Mirallès, F., Pouliot, N., Montambault, S.. State-of-the-art review of computer vision for the management of power transmission lines. Proceedings of the 2014 3rd International Conference on Applied Robotics for the Power Industry (CARPI). : 1-6
Zhang, J., Liu, L., Wang, B., Chen, X., Wang, Q., Zheng, T.. High speed automatic power line detection and tracking for a UAV-based inspection. Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering (ICICEE). : 266-269
Sampedro, C., Martinez, C., Chauhan, A., Campoy, P.. A supervised approach to electric tower detection and classification for power line inspection. Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN). : 1970-1977
Wronkowicz, A.. Vision Diagnostics of Power Transmission Lines: Approach to Recognition of Insulators. Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. 2016: 431-440
Yan, T., Yang, G., Yu, J.. Feature fusion based insulator detection for aerial inspection. Proceedings of the 2017 36th Chinese Control Conference (CCC). : 10972-10977
Zhao, Z., Liu, N.. The recognition and localization of insulators adopting SURF and IFS based on correlation coefficient. Opt. Int. J. Light Electron Opt.. 2014; 125: 6049-6052
Zhao, Z., Liu, N., Wang, L.. Localization of multiple insulators by orientation angle detection and binary shape prior knowledge. IEEE Trans. Dielectr. Electr. Insul.. 2015; 22: 3421-3428
Zhao, Z., Xu, G., Qi, Y., Liu, N., Zhang, T.. Multi-patch deep features for power line insulator status classification from aerial images. Proceedings of the 2016 International Joint Conference on Neural Networks, IJCNN 2016. : 3187-3194
Liu, Y., Yong, J., Liu, L., Zhao, J., Li, Z.. The method of insulator recognition based on deep learning. Proceedings of the 2016 4th International Conference on Applied Robotics for the Power Industry (CARPI). : 1-5
Levinshtein, A., Stere, A., Kutulakos, K.N., Fleet, D.J., Dickinson, S.J., Siddiqi, K.. TurboPixels: Fast Superpixels Using Geometric Flows. IEEE Trans. Pattern Anal. Mach. Intell.. 2009; 31: 2290-2297
Zitnick, C.L., Dollár, P., Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T.. Edge Boxes: Locating Object Proposals from Edges. Proceedings of the European Conference on Computer Vision, ECCV 2014. 2014: 391-405
Ren, S., He, K., Girshick, R., Sun, J., Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R.. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems 28. 2015: 91-99
Ren, S., He, K., Girshick, R., Sun, J.. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans. Pattern Anal. Mach. Intell.. 2017; 39: 1137-1149
Uijlings, J., van de Sande, K., Gevers, T., Smeulders, A.. Selective Search for Object Recognition. Int. J. Comput. Vis.. 2013; 104: 154-171
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M.. TensorFlow: A System for Large-Scale Machine Learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). ; Volume 16: 265-283
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.. Microsoft COCO: Common objects in context. Proceedings of the European Conference on Computer Vision. 2014: 740-755
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BibTeX entry
@article{oai:it.cnr:prodotti:404156,
title = {Fault detection in power equipment via an unmanned aerial system using multi modal data},
author = {Jalil B. and Leone G. R. and Martinelli M. and Moroni M. and Pascali M. A. and Berton A.},
publisher = {Molecular Diversity Preservation International (MDPI),, Basel },
doi = {10.3390/s19133014},
journal = {Sensors (Basel)},
volume = {19},
year = {2019}
}
0000-0002-2930-7554
