Intruder Detection by Using Faster R-CNN in Power Substation

  • Krit Srijakkot
  • Isoon Kanjanasurat
  • Nuttakan Wiriyakrieng
  • Mayulee Lartwatechakul
  • Chawalit BenjangkaprasertEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1149)


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%.


Faster R-CNN Intruder detection Object detection 



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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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Krit Srijakkot
    • 1
  • Isoon Kanjanasurat
    • 1
  • Nuttakan Wiriyakrieng
    • 1
  • Mayulee Lartwatechakul
    • 1
  • Chawalit Benjangkaprasert
    • 1
    Email author
  1. 1.Department of Computer Engineering, Faculty of EngineeringKing Mongkut’s Institute of Technology LadkrabangBangkokThailand

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