Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic Robotic Environments

Victor A. Kich, Muhammad A. Muttaqien, Junya Toyama, Ryutaro Miyoshi, Yosuke Ida, Akihisa Ohya, Hisashi Date

TL;DR

This paper evaluates YOLOv5 and YOLOv8 in dynamic robotic environments inspired by the Tsukuba Challenge, challenging the presumption that newer YOLO iterations inherently yield better performance. Using a curated robotic dataset, targeted optimizations, and an ablation framework, the study shows that YOLOv5 variants can achieve equal or superior precision compared to YOLOv8 in real-world conditions. The findings emphasize the critical roles of dataset characteristics, training procedures, and optimization techniques in determining practical performance for robotic perception. The work advocates for application-driven model selection and thorough, context-aware evaluation to maximize efficiency and reliability in autonomous robotic systems.

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.

Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic Robotic Environments

TL;DR

This paper evaluates YOLOv5 and YOLOv8 in dynamic robotic environments inspired by the Tsukuba Challenge, challenging the presumption that newer YOLO iterations inherently yield better performance. Using a curated robotic dataset, targeted optimizations, and an ablation framework, the study shows that YOLOv5 variants can achieve equal or superior precision compared to YOLOv8 in real-world conditions. The findings emphasize the critical roles of dataset characteristics, training procedures, and optimization techniques in determining practical performance for robotic perception. The work advocates for application-driven model selection and thorough, context-aware evaluation to maximize efficiency and reliability in autonomous robotic systems.

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.
Paper Structure (12 sections, 4 figures, 1 table)

This paper contains 12 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methodology
  4. Optimization Techniques
  5. Data Preparation and Model Training
  6. Ablation Insights
  7. Ablation Study
  8. Performance Evaluation
  9. Analysis of Performance Metrics
  10. Implications for Robotic Applications
  11. Insights from Comparative Performance
  12. Conclusion

Figures (4)

  • Figure 1: Image samples from the proposed dataset designed to train various YOLO models for real-time object detection
  • Figure 2: The vision system of the Kerberos Robot utilizes three cameras to identify and categorize the designated target box. The procedure includes cropping the area of interest, implementing a mask over the captured box to isolate it, and finally, engaging a specialized neural network for letter classification. This streamlined workflow ensures precise detection and analysis.
  • Figure 3: The overview of YOLOv8 architecture.
  • Figure 4: Performance metrics for YOLOv8 and YOLOv5 during training and validation over several epochs. The legends for each graph include variations of the model: 'm', 'l', and 'x', representing different model sizes or configurations, with 'train' indicating training data and 'val' indicating validation data.