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드론의 변전소 애자 이상현상 탐지를 위한 심층신경망 경량화 모델 기반 알고리즘 개발 연구
오선택(SeonTaek Oh),김형우(HyungWoo Kim),조성현(SungHyun Cho),유지환(JiHwan You),권용성(Youngsung Kwon),나원상(Won-Sang Ra),김영근(Young-Keun Kim) 제어로봇시스템학회 2020 제어·로봇·시스템학회 논문지 Vol.26 No.11
This paper proposes a method to detect defected insulators of electrical substations in real-time on an embedded graphics processing unit (GPU) by using a drone camera. The proposed algorithm is based on a compressed deep neural network to detect defected insulators with a corona effect arising from an overcurrent. A compressed deep neural network model is developed to reduce the computation time while maintaining high detection accuracy for the embedded GPU board. This paper applies the MobileNetv2 structure and compression technique, a residual block, on YOLOv3 to reduce the overall memory size and calculation time of the detection architecture. The proposed network is trained with an image data set of a mockup insulator with and without the corona effect. The performance evaluation shows that the algorithm is able to detect the object with an accuracy of 85% at an average speed of 40 FPS on a NVIDIA Xavier board. In addition, the results show that the proposed detection network uses half the memory and less computation time than YOLOv3-Tiny.