FPGAを用いたDeep Learningによる自動車ナンバープレートの認識

抄録

Today, training and execution of deep learning is almost carried out on GPU because of extensive calculation. However, using GPU have problems of power consumption and portability. This paper presents the way to recognize of automobile license plate and number using deep learning on FPGA. Binarized Neural Network to reduce calculation on PYNQ-Z1 board to design of hardware is used for the image processing. As a result, the recognition accuracy of automobile license plate was 84%, and the recognition accuracy of number was 98%. In addition, processing speed of hardware was 1,604 microseconds and it had about five hundred times more than CPU.