YOLOv5优化版在边缘FPGA上实现99%准确率实时检测
Real-Time Object Detection and Classification using YOLO for Edge FPGAs
- 针对FPGA优化YOLOv5，提升资源效率
- 实测达99%分类准确率，3.5W功耗下9FPS
- 适合对功耗和速度敏感的边缘智能场景
目标检测与分类在诸多应用领域至关重要，尤其在安全可靠的高级驾驶辅助系统（ADAS）开发中。现有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）在部署于现场可编程门阵列（FPGA）时已展现高精度与计算速度。然而，即便如此，当前最先进的基于YOLO的检测与分类系统仍难以在边缘FPGA平台实现足够的资源效率。为此，本文提出一种面向FPGA部署的资源高效实时目标检测与分类系统，基于优化后的YOLOv5。该系统在COCO与GTSRD数据集上训练，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，处理速度达9帧每秒（FPS）。这些发现证明了该方法在支持边缘计算应用中的实时性与资源高效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。