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.

随着人工智能技术的进步，特别是深度学习与硬件平台（如现场可编程门阵列（FPGA）和图形处理单元）在各个领域提供了有效的成果。具有可重新配置架构的 FPGA 提供了灵活性、更好的性能和高水平的并行性。目标检测是计算机视觉和图像处理应用领域的主要研究领域之一。You Only Look Once (YOLO) 是一种快速准确的最先进的对象检测算法。然而，许多应用需要准确和快速处理以获得更好的结果。对于这种情况，这些算法可以在硬件加速器上实现。本工作提出在 Xilinx® 上实现 YOLOv4^算法Zynq-7000 片上系统，适用于实时物体检测。拟议的工作显示出更好的资源利用率，大约 23.2 k (43.6%) 的查找表、45.8 k (43.04%) 的触发器、115 (82.17%) 个 BRAM 和 174 (79%) 个 DSP，在 100 MHz 频率下实现与其他模拟结果相比，效率更高。