Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic Robotic Environments

TL;DR

This paper compares YOLOv5 and YOLOv8 in robotic environments, revealing that YOLOv5 often matches or surpasses YOLOv8 in precision, challenging assumptions about their relative performance and emphasizing the importance of context in model selection.

Contribution

The study provides a comprehensive comparison of YOLOv5 and YOLOv8, highlighting the circumstances under which YOLOv5 performs equally or better, and offers insights into optimizing object detection for robotics.

Findings

YOLOv5 shows comparable or superior precision to YOLOv8 in certain tasks.

Model architecture and training data significantly influence detection performance.

Ablation studies reveal factors affecting model adaptability in real-world scenarios.

Abstract

Recent advancements in real-time object detection frameworks have spurred extensive research into their application in robotic systems. This study provides a comparative analysis of YOLOv5 and YOLOv8 models, challenging the prevailing assumption of the latter's superiority in performance metrics. Contrary to initial expectations, YOLOv5 models demonstrated comparable, and in some cases superior, precision in object detection tasks. Our analysis delves into the underlying factors contributing to these findings, examining aspects such as model architecture complexity, training dataset variances, and real-world applicability. Through rigorous testing and an ablation study, we present a nuanced understanding of each model's capabilities, offering insights into the selection and optimization of object detection frameworks for robotic applications. Implications of this research extend to the design of more efficient and contextually adaptive systems, emphasizing the necessity for a holistic approach to evaluating model performance.