Inspección de subestaciones eléctricas: YOLOv5 en la identificación de puntos calientes mediante imágenes térmicas
Contenido principal del artículo
Resumen
Detalles del artículo

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
La Universidad Politécnica Salesiana de Ecuador conserva los derechos patrimoniales (copyright) de las obras publicadas y favorecerá la reutilización de las mismas. Las obras se publican en la edición electrónica de la revista bajo una licencia Creative Commons Reconocimiento / No Comercial-Sin Obra Derivada 4.0 Ecuador: se pueden copiar, usar, difundir, transmitir y exponer públicamente.
El autor/es abajo firmante transfiere parcialmente los derechos de propiedad (copyright) del presente trabajo a la Universidad Politécnica Salesiana del Ecuador, para las ediciones impresas.
Se declara además haber respetado los principios éticos de investigación y estar libre de cualquier conflicto de intereses.
El autor/es certifican que este trabajo no ha sido publicado, ni está en vías de consideración para su publicación en ninguna otra revista u obra editorial.
El autor/es se responsabilizan de su contenido y de haber contribuido a la concepción, diseño y realización del trabajo, análisis e interpretación de datos, y de haber participado en la redacción del texto y sus revisiones, así como en la aprobación de la versión que finalmente se remite en adjunto.
Referencias
S. Hussain, J. Hernández Fernández, A. K. Al-Ali, and A. Shikfa, “Vulnerabilities and countermeasures in electrical substations,” International Journal of Critical Infrastructure Protection, vol. 33, p. 100406, 2021. [Online]. Available: https://doi.org/10.1016/J.IJCIP.2020.100406
Y.-E. Bouffard-Vercelli and B. André, “Future architectures of electrical substations,” in 2021 Petroleum and Chemical Industry Conference Europe (PCIC Europe), 2021. [Online]. Available: https://doi.org/10.23919/PCICEurope50407.2021.9805424
W. Pavon, E. Inga, S. Simani, and M. Nonato, “A review on optimal control for the smart grid electrical substation enhancing transition stability,” Energies, vol. 14, no. 24, 2021. [Online]. Available: https://doi.org/10.3390/en14248451
M. Lin, L. Fu, F. Zeng, G. Yang, and M. Sun, “Design of distributed substation high voltage electrical equipment online monitoring system based on image segmentation technology,” Journal of Physics: Conference Series, vol. 2143, no. 1, p. 012001, dec 2021. [Online]. Available: https://dx.doi.org/10.1088/1742-6596/2143/1/012001
M. A. Haq, D. Kurniawan Danu, Syafii, and Muhardika, “Mitigation of the potential for sudden high-temperature hotspots on substation equipment,” in 2023 4th International Conference on High Voltage Engineering and Power Systems (ICHVEPS), 2023, pp. 194–198. [Online]. Available: https://doi.org/10.1109/ICHVEPS58902.2023.10257349
S. Y. Lee and S. S. Teoh, “A survey on infrared thermography based automatic electrical fault diagnosis techniques,” in 10th International Conference on Robotics, Vision, Signal Processing and Power Applications, M. A. M. Zawawi, S. S. Teoh, N. B. Abdullah, and M. I. S. Mohd Sazali, Eds. Singapore: Springer Singapore, 2019, pp. 537–542. [Online]. Available: https://doi.org/10.1007/978-981-13-6447-1_68
F. Ciampa, P. Mahmoodi, F. Pinto, and M. Meo, “Recent advances in active infrared thermography for non-destructive testing of aerospace components,” Sensors, vol. 18, no. 2, 2018. [Online]. Available: https://doi.org/10.3390/s18020609
M. Haenlein and A. Kaplan, “A brief history of artificial intelligence: On the past, present, and future of artificial intelligence,” California Management Review, vol. 61, no. 4, pp. 5–14, 2019. [Online]. Available: https://doi.org/10.1177/0008125619864925
A. Ghahramani, G. Castro, S. A. Karvigh, and B. Becerik-Gerber, “Towards unsupervised learning of thermal comfort using infrared thermography,” Applied Energy, vol. 211, pp. 41–49, 2018. [Online]. Available: https://doi.org/10.1016/j.apenergy.2017.11.021
Y. J. Wai, Z. bin Mohd Yussof, S. I. bin Salim, and L. K. Chuan, “Fixed point implementation of Tiny-YOLO-v2 using OpenCL on FPGA,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 10, 2018. [Online]. Available: http://dx.doi.org/10.14569/IJACSA.2018.091062
Y. Xiao, Z. Tian, J. Yu, Y. Zhang, S. Liu, S. Du, and X. Lan, “A review of object detection based on deep learning,” Multimedia Tools and Applications, vol. 79, no. 33, pp. 23 729–23 791, Sep 2020. [Online]. Available: https://doi.org/10.1007/s11042-020-08976-6
S. Srivastava, A. V. Divekar, C. Anilkumar, I. Naik, V. Kulkarni, and V. Pattabiraman, “Comparative analysis of deep learning image detection algorithms,” Journal of Big Data, vol. 8, no. 1, p. 66, May 2021. [Online]. Available: https://doi.org/10.1186/s40537-021-00434-w
D. Dlužnevskij, P. Stefanovic, and S. Ramanauskaite, “Investigation of YOLOv5 efficiency in iphone supported systems,” Baltic Journal of Modern Computing, vol. 9, no. 3, pp. 333–344, 2021. [Online]. Available: https://doi.org/10.22364/bjmc.2021.9.3.07
Z. Ma, Y. Wan, J. Liu, R. An, and L. Wu, “A kind of water surface multi-scale object detection method based on improved YOLOv5 network,” Mathematics, vol. 11, no. 13, 2023. [Online]. Available: https://doi.org/10.3390/math11132936
G. Liu, J. C. Nouaze, P. L. Touko Mbouembe, and J. H. Kim, “YOLO-Tomato: A robust algorithm for tomato detection based on YOLOv3,” Sensors, vol. 20, no. 7, 2020. [Online]. Available: https://doi.org/10.3390/s20072145
X. Gong, Q. Yao, M. Wang, and Y. Lin, “A deep learning approach for oriented electrical equipment detection in thermal images,” IEEE Access, vol. 6, pp. 41 590–41 597, 2018. [Online]. Available: https://doi.org/10.1109/ACCESS.2018.2859048
X. Li, “Design of infrared anomaly detection for power equipment based on YOLOv3,” in 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), 2019, pp. 2291–2294. [Online]. Available: https://doi.org/10.1109/EI247390.2019.9061852
A. Greco, C. Pironti, A. Saggese, M. Vento, and V. Vigilante, “A deep learning based approach for detecting panels in photovoltaic plants,” in Proceedings of the 3rd International Conference on Applications of Intelligent Systems, ser. APPIS
New York, NY, USA: Association for Computing Machinery, 2020. [Online]. Available: https://doi.org/10.1145/3378184.3378185
D. T. Nguyen, T. N. Nguyen, H. Kim, and H.-J. Lee, “A high-throughput and power-efficient FPGA implementation of YOLO CNN for object detection,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 27, no. 8, pp. 1861–1873, 2019. [Online]. Available: https://doi.org/10.1109/TVLSI.2019.2905242
D. Fan, D. Liu, W. Chi, X. Liu, and Y. Li, “Improved ssd-based multi-scale pedestrian detection algorithm,” in Advances in
D Image and Graphics Representation, Analysis, Computing and Information Technology, R. Kountchev, S. Patnaik, J. Shi, and M. N. Favorskaya, Eds. Singapore: Springer Singapore, 2020, pp. 109–118. [Online]. Available: https://doi.org/10.1007/978-981-15-3867-4_14
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, 2017. [Online]. Available: https://doi.org/10.1109/TPAMI.2016.2577031
W. Chen, H. Huang, S. Peng, C. Zhou, and C. Zhang, “YOLO-face: a real-time face detector,” The Visual Computer, vol. 37, no. 4, pp. 805–813, Apr 2021. [Online]. Available: https://doi.org/10.1007/s00371-020-01831-7
S. A. Sánchez, H. J. Romero, and A. D. Morales, “A review: Comparison of performance metrics of pretrained models for object detection using the tensorflow framework,” IOP Conference Series: Materials Science and Engineering, vol. 844, no. 1, p. 012024, may 2020. [Online]. Available: https://dx.doi.org/10.1088/1757-899X/844/1/012024
J. Xue, F. Cheng, Y. Li, Y. Song, and T. Mao, “Detection of farmland obstacles based on an improved YOLOv5s algorithm by using CIoU and anchor box scale clustering,” Sensors, vol. 22, no. 5, 2022. [Online]. Available: https://doi.org/10.3390/s22051790
A. Li, S. Sun, Z. Zhang, M. Feng, C. Wu, and W. Li, “A multi-scale traffic object detection algorithm for road scenes based on improved YOLOv5,” Electronics, vol. 12, no. 4, 2023. [Online]. Available: https://doi.org/10.3390/electronics12040878
J. Shi, J. Yang, and Y. Zhang, “Research on steel surface defect detection based on YOLOv5 with attention mechanism,” Electronics, vol. 11, no. 22, 2022. [Online]. Available: https://doi.org/10.3390/electronics11223735
D. A. Pérez-Aguilar, R. H. Risco-Ramos, and L. Casaverde-Pacherrez, “Transfer learning en la clasificación binaria de imágenes térmicas,” INGENIUS, no. 26, pp. 71–86, 2021. [Online]. Available: https://doi.org/10.17163/ings.n26.2021.07
A. Yan-Tak Ng. (2022) Unbiggen AI. IEEE Spectrum. IEEE Spectrum. [Online]. Available: https://bit.ly/3RNNvsr
R. Padilla, W. L. Passos, T. L. B. Días, S. L. Netto, and E. A. B. da Silva, “A comparative analysis of object detection metrics with a companion open-source toolkit,” Electronics, vol. 10, no. 3, 2021. [Online]. Available: https://doi.org/10.3390/electronics10030279