Comparative Analysis of Deep Learning Models for Perception in Autonomous Vehicles
This incremental comparison helps the autonomous vehicle research community select models for perception tasks.
The paper compared YOLO-NAS and YOLOv8 deep learning models for object detection in autonomous vehicles using a custom dataset, finding that YOLOv8s saved 75% training time and achieved 83% accuracy versus 81% for YOLO-NAS.
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.