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Author: search.filters.author.Lartwatechakul, MayuleeHas files: No

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    Item type:Publication,
    Intruder Detection by Using Faster R-CNN in Power Substation
    (2020-01-01)
    Srijakkot, Krit
    ;
    Kanjanasurat, Isoon
    ;
    Wiriyakrieng, Nuttakan
    ;
    Lartwatechakul, Mayulee
    ;
    Benjangkaprasert, Chawalit
    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%.