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

1.School of Information Science and Technology, Shanghai Tech University, Shanghai 201210, China;2.Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 20083, China;3.University of Chinese Academy of Sciences, Beijing 100049, China;4.Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China

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Supported by the National Pre-Research Foundation of China during the “14th Five-Year Plan” (514010405-207)

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    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 driving. In this paper, a lightweight target detection algorithm for embedded devices is proposed, which is accelerated 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 improves performance by 2.59× and reduces 49.02% of the power consumption.

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CHEN Shao-Yi, TANG Xin-Yi, WANG Jian, HUANG Jing-Si, LI Zheng. An ultra-efficient streaming-based FPGA accelerator for infrared target detection[J]. Journal of Infrared and Millimeter Waves,2022,41(5):914~922

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  • Received:January 13,2022
  • Revised:September 08,2022
  • Adopted:March 29,2022
  • Online: September 05,2022
  • Published: