  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.
