# Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic Robotic Environments

arXiv: https://arxiv.org/abs/2406.00315 · HTML: https://mlsanity.com/paper/2406.00315/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 1 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Tiny-YOLO object detection supplemented with geometrical data](https://mlsanity.com/paper/2008.02170/) (2020)

## Built on by (most cited first, 2 of 2)

- [NutrifyAI: An AI-Powered System for Real-Time Food Detection, Nutritional Analysis, and Personalized Meal Recommendations](https://mlsanity.com/paper/2408.10532/) (2024)
- [Deep Learning Perspective of Scene Understanding in Autonomous Robots](https://mlsanity.com/paper/2512.14020/) (2025)

## Abstract (arXiv)

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.