基于FPGA的目标检测系统与加速器设计

Design of Object Detection System and Accelerator Based on FPGA

目标检测作为计算机视觉技术的基础任务,在智慧医疗、智能交通等生活场景中应用广泛。深度学习具有高类别检测精度、高精度定位的优势,是当前目标检测的研究重点。由于卷积神经网络计算复杂度高、内存要求高,使用CPU实现的设计方案已经难以满足实际应用的需求。现场可编程逻辑门阵列(FPGA)具有可重构、高能效、低延迟的特点。研究围绕FPAG硬件设计,选取了YOLOv2算法,并针对该算法设计了对应的硬件加速器,实现了基于FPGA的目标检测。

As a basic task of computer vision technology,object detection is widely used in smart medicine,intelligent transportation and other life scenes.Deep Learning nowadays becomes the research focus of object detection for its advantages of high precision in class detection and positioning.Due to the great computational complexity and memory requirements,the design scheme implemented by using the CPU has been difficult to meet the needs of practical application.FPGA has the characteristics of reconfigurability,high energy efficiency and low latency.The research focuses on the FPGA hardware design,selects the YOLOv2 algorithm,designs the corresponding hardware accelerator in terms of the algorithm,and realizes the object detection based on FPGA.