Abstract
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.
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