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.

翻译：近年来，大量机器学习（ML）与深度学习（DL）算法被提出，旨在实现自动驾驶汽车（AVs）的高效性、安全性与可靠性。自动驾驶汽车利用感知系统来检测、定位并识别其他车辆、行人及道路标志，以执行安全的导航与决策任务。本文比较了包括YOLO-NAS和YOLOv8在内的深度学习模型在基于检测的感知任务上的性能。我们采集了自定义数据集，并利用该数据集对两种深度学习模型进行了实验。我们的分析表明，相较于YOLO-NAS模型，YOLOv8s模型节省了75%的训练时间。此外，当以获取最高目标检测精度为目标时，YOLOv8s模型（83%）的表现优于YOLO-NAS模型（81%）。对这些新兴深度学习模型的比较分析，将有助于相关研究社群理解这些模型在真实应用场景下的性能表现。