An ultra-efficient streaming-based FPGA accelerator for infrared target detection

Object detection algorithm based on deep learning has achieved great success,significantly better than the effect of traditional algorithms,and even surpassed human in many scenarios. Unlike RGB cameras,infrared cameras can see objects even in the dark,which can be used in many fields like surveillance and autonomous driv. ing. In this paper,a lightweight target detection algorithm for embedded devices is proposed,which is accelerat. ed and deployed using Xilinx Ultrascale+MPSoC FPGA ZU3EG. The accelerator runs at a 350 MHz frequency clock with throughput of 551 FPS and power of only 8. 4 W. The intersection over union(IoU)of the algorithm achieves an accuracy of 73. 6% on FILR datasets. Comparing with the previous work,the accelerator design im. proves performance by 2. 59x and reduces 49. 02% of the power consumption.