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

TL;DR

This paper introduces a resource-efficient YOLOv5-based system optimized for FPGA deployment, achieving high accuracy and real-time processing for edge applications like ADAS.

Contribution

It presents a novel FPGA-optimized YOLOv5 implementation that balances accuracy and resource efficiency for real-time edge object detection.

Findings

99% classification accuracy

Power consumption of 3.5W

Processing speed of 9 FPS

Abstract

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.