对比YOLOv8与YOLO-NAS在自动驾驶感知中的表现
Comparative Analysis of Deep Learning Models for Perception in Autonomous Vehicles
- 用自建数据集测试YOLOv8s与YOLO-NAS的检测性能
- YOLOv8s训练时间少75%，准确率83%优于YOLO-NAS的81%
- 适合关注实时性与精度平衡的自动驾驶研发人员
近年来，大量机器学习（ML）和深度学习（DL）算法被提出，以提升自动驾驶车辆（AV）的效率、安全性和可靠性。自动驾驶系统依赖感知模块来检测、定位并识别其他车辆、行人及道路标志，从而实现安全导航与决策。本文比较了YOLO-NAS与YOLOv8等深度学习模型在基于检测的感知任务中的表现。研究团队采集了自定义数据集，并在此基础上对两种模型进行实验。分析显示，YOLOv8s模型相比YOLO-NAS模型训练时间减少75%；在目标为最高检测准确率时，YOLOv8s达到83%，优于YOLO-NAS的81%。这些对比分析有助于相关研究社区理解新兴深度学习模型在真实场景下的性能表现。
原文摘要 · Abstract (English)
Recently, a plethora of machine learning (ML) and deep learning (DL) algorithms have been proposed to achieve the efficiency, safety, and reliability of autonomous vehicles (AVs). The AVs use a perception system to detect, localize, and identify other vehicles, pedestrians, and road signs to perform safe navigation and decision-making. In this paper, we compare the performance of DL models, including YOLO-NAS and YOLOv8, for a detection-based perception task. We capture a custom dataset and experiment with both DL models using our custom dataset. Our analysis reveals that the YOLOv8s model saves 75% of training time compared to the YOLO-NAS model. In addition, the YOLOv8s model (83%) outperforms the YOLO-NAS model (81%) when the target is to achieve the highest object detection accuracy. These comparative analyses of these new emerging DL models will allow the relevant research community to understand the models' performance under real-world use case scenarios.
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