标题： 基于边缘FPGA的实时目标检测与分类使用YOLO 显示英文标题

标题： Real-Time Object Detection and Classification using YOLO for Edge FPGAs

摘要： 目标检测和分类是各个应用领域中的关键任务，尤其是在开发安全可靠的高级驾驶辅助系统（ADAS）中。现有的基于深度学习的方法，如卷积神经网络（CNN）、单次检测器（SSD）和你只看一次（YOLO）在部署到现场可编程门阵列（FPGAs）时，在准确性和计算速度方面表现出色。然而，尽管有这些进展，最先进的基于YOLO的目标检测和分类系统在实现适合边缘FPGA平台的资源效率方面仍然面临挑战。为了解决这一限制，本文提出了一种基于YOLOv5优化的资源高效实时目标检测和分类系统，适用于FPGA部署。该系统在COCO和GTSRD数据集上进行训练，并在Xilinx Kria KV260 FPGA板上实现。实验结果表明，分类准确率达到99%，功耗为3.5W，处理速度为每秒9帧（FPS）。这些发现突显了所提出方法在为边缘计算应用实现实时、资源高效的目標检测和分类方面的有效性。 显示英文摘要

摘要： Object detection and classification are crucial tasks across various application domains, particularly in the development of safe and reliable Advanced Driver Assistance Systems (ADAS). Existing deep learning-based methods such as Convolutional Neural Networks (CNNs), Single Shot Detectors (SSDs), and You Only Look Once (YOLO) have demonstrated high performance in terms of accuracy and computational speed when deployed on Field-Programmable Gate Arrays (FPGAs). However, despite these advances, state-of-the-art YOLO-based object detection and classification systems continue to face challenges in achieving resource efficiency suitable for edge FPGA platforms. To address this limitation, this paper presents a resource-efficient real-time object detection and classification system based on YOLOv5 optimized for FPGA deployment. The proposed system is trained on the COCO and GTSRD datasets and implemented on the Xilinx Kria KV260 FPGA board. Experimental results demonstrate a classification accuracy of 99%, with a power consumption of 3.5W and a processing speed of 9 frames per second (FPS). These findings highlight the effectiveness of the proposed approach in enabling real-time, resource-efficient object detection and classification for edge computing applications.