Power Defect Recognition Method Based on Fixed-point Adaptive Selection Convolution Neural Network

The image data of unmanned aerial vehicle (UAV) during power inspection increase sharply. In this paper, we focus on improving the traditional deep convolutional neural network (DCNN) algorithm to reduce the computational cost of DCNN model in airborne front-end platform, to effectively improve the speed of power defect identification, and to extend the range of inspection UAV. Firstly, the floating-point operation in convolution network is approximated by fixed-point, and then the input image of DCNN model is adaptively selected by fast machine learning algorithm. Finally, the proposed method is verified by experiments. The experimental results show that the accuracy of the DCNN model which is optimized by 8 bit fixed-point and adaptive selection strategy is 88.2%, the reasoning time is reduced by 65.9%, the energy consumption is reduced by 71.9%, and the precision is improved by 9.8%. The adaptive selection strategy of the fixed-point DCNN model designed in this paper can not only save the power consumption of power inspection system, but also improve the accuracy of power defect identification. © 2021, High Voltage Engineering Editorial Department of CEPRI. All right reserved.