Comparative Analysis of Deep Learning Models for Perception in Autonomous Vehicles

TL;DR

This paper compares the performance of YOLO-NAS and YOLOv8 deep learning models for perception tasks in autonomous vehicles, highlighting training efficiency and detection accuracy using a custom dataset.

Contribution

It provides a comparative analysis of YOLO-NAS and YOLOv8 models on a custom dataset for autonomous vehicle perception, emphasizing training time and detection accuracy.

Findings

YOLOv8s saves 75% of training time compared to YOLO-NAS.

YOLOv8s achieves 83% detection accuracy, outperforming YOLO-NAS at 81%.

The analysis helps understand model performance in real-world autonomous vehicle scenarios.

Abstract

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.