Abstract
This paper presents the intruder detection by using the Faster R-CNN model and administrator system for the power substation in Khon Kaen substation 4 of the Electricity Generating Authority of Thailand (EGAT). There are two processes of intruder detection-detecting the intruder and sending a notification to the system administrator of EGAT through Line application. The Faster R-CNN model of intruder detection was trained and tested by using the Open Image Dataset and our dataset. We collected our dataset of 1,500 images from a different condition from the real environment. There are two conditions, including distance and light intensity. Our system used a high-performance computer by using GPU: Nvidia Titan RTX 24 GB to support the object detection system from using five cameras at the same time. The performance of intruder detection achieved by greater than 95%.
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References
Zhang, H., Du, Y., Ning, S., Zhang, Y., Yang, S., Du, C.: Pedestrain detection method based on faster R-CNN. In: International Conference on Computational Intelligence and Security, pp. 427–430 (2017)
Kannojia, S.P., Jaiswal, G.: Ensemble of hybrid CNN-ELM model for image classification. In: International Conference on Signal Processing and Integrated Networks, pp. 538–541 (2018)
Zhang, W., Wang, S., Thachan, S., Chen, J., Qian, Y.: Deconv R-CNN for small object detection on remote sensing images. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 2483–2486 (2018)
Wang, X.Z., Lu, S.X.: Improved fuzzy multicategory support vector machines classifier. In: Fifth International Conference on Machine Learning and Cybernetics, pp. 3585–3589 (2006)
Shih, C.H., Chung, L.H., Cheng H.C.: Vehicle detection using simplified fast R-CNN. In: International Workshop on Advanced Image Technology, pp. 1–3 (2018)
Gavrilescu, R., Zet, C., Fosalau, C., Skoczylas, M., Cotovanu, D.: Faster R-CNN: an approach to real-time object detection. In: International Conference and Exposition on Electrical and Power Engineering, pp. 165–168 (2018)
Liu, B., Zhao, W., Sun, Q.: Study of object detection based on faster R-CNN. In: Chinese Automation Congress, pp. 6233–6236 (2017)
Hsu, S.C., Wang, Y.W., Huang, C.L.: Human object identification for human-robot interaction by using fast R-CNN. In: Second IEEE International Conference on Robotic Computing, pp. 201–204 (2018)
Wang, Y., Zhang, J., Zhu, L., Sun, Z., Lu, J.: A moving object detection scheme based on video surveillance for smart substation. In: 14th IEEE International Conference on Signal Processing, pp. 500–503 (2018)
Li, K., Huang, Z., Cheng, Y.C., Lee, C.H.: A maximal figure-of-merit learning approach to maximizing mean average precision with deep neural network based classifiers. In: IEEE International Conference on Acoustic, Speech and Signal Processing, pp. 4503–4507 (2014)
Mohan, A.S., Resmi, R.: Video image processing for moving object detection and segmentation using background subtraction. In: First International Conference on Computational Systems and Communications, pp. 288–292 (2014)
Kim, I., Lee, C.H.: Optimization of average precision with maximal figure-of-merit learning. In: IEEE International Workshop on Machine Learning for Signal Processing, pp. 1–6 (2011)
Niitani, Y., Akiba, T., Kerola, T., Ogawa, T., Sano, S., Suzuki, S.: Sampling techniques for large-scale object detection from sparsely annotated objects. In: Computer Vision and Pattern Recognition, pp. 6510–6518 (2019)
Acknowledgments
This research was supported by The Electricity Generating Authority of Thailand (EGAT). We thank our colleagues from King Mongkut’s Institute of Technology Ladkrabang (KMITL) who provided insight and expertise that greatly assisted the research.
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Srijakkot, K., Kanjanasurat, I., Wiriyakrieng, N., Lartwatechakul, M., Benjangkaprasert, C. (2020). Intruder Detection by Using Faster R-CNN in Power Substation. In: Meesad, P., Sodsee, S. (eds) Recent Advances in Information and Communication Technology 2020. IC2IT 2020. Advances in Intelligent Systems and Computing, vol 1149. Springer, Cham. https://doi.org/10.1007/978-3-030-44044-2_16
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DOI: https://doi.org/10.1007/978-3-030-44044-2_16
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