Target Detection System Design and FPGA Implementation Based on YOLO v2 Algorithm

This paper proposes Target detection system design and FPGA implementation based on YOLO v2 algorithm, in order to realize offline real-time image detection in a platform with limited resources and power consumption. First, this paper studied the algorithm of YOLO v2 convolutional neural network, designed and trained the neural network. Secondly, a special floating-point number matrix multiplication unit and a double-cache data processing circuit are designed to improve the calculation speed and cell utilization efficiency of floating-point number matrix multiplication in convolutional neural network, and further accelerate the target detection speed on the hardware level. In this paper, the above scheme is implemented at the board level. The test results show that the average recognition speed is 50frame/s under the offline state, which basically achieves the design goal of real-time detection.