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Hardware acceleration for object detection using YOLOv4 algorithm on Xilinx Zynq platform

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Abstract

With the technological improvement in artificial intelligence, particularly deep learning is providing effective outcomes along with hardware platforms such as field-programmable gate arrays (FPGAs) and graphics processing units in various domains. FPGAs with their reconfigurable architectures provide flexibility, better performance and high levels of parallelism. Object detection is one of the prominent areas of research in the fields of computer vision and image processing applications. You Only Look Once (YOLO) is a state-of-the art object detection algorithm which is fast and accurate. However, many applications require accuracy and rapid processing for better results. For such conditions, these algorithms can be implemented on hardware accelerators. This work proposes the implementation of YOLOv4 algorithm on Xilinx® Zynq-7000 System on a chip and is suitable for real-time object detection. The proposed work shows better resource utilization of about 23.2 k (43.6%) of Look-up tables, 45.8 k (43.04%) of Flip-flops, 115 (82.17%) BRAMs and 174 (79%) DSPs achieving at 100 MHz frequency which is more efficient on comparing with other simulation results.

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Acknowledgements

The authors would like to thank SRM Institute of Science and Technology for providing facilities to carry out the research work.

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  1. Hindustan Institute of Technology and Science, Chennai, India

    Praveenkumar Babu

  2. SRM Institute of Science and Technology, Kattankulathur, Chennai, India

    Eswaran Parthasarathy

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  1. Praveenkumar Babu
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  2. Eswaran Parthasarathy
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Correspondence to Eswaran Parthasarathy.

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Babu, P., Parthasarathy, E. Hardware acceleration for object detection using YOLOv4 algorithm on Xilinx Zynq platform. J Real-Time Image Proc 19, 931–940 (2022). https://doi.org/10.1007/s11554-022-01234-y

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  • DOI: https://doi.org/10.1007/s11554-022-01234-y

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