  This study presents a comprehensive analysis of the YOLOv5 object detection
model, examining its architecture, training methodologies, and performance. Key
components, including the Cross Stage Partial backbone and Path
Aggregation-Network, are explored in detail. The paper reviews the model's
performance across various metrics and hardware platforms. Additionally, the
study discusses the transition from Darknet to PyTorch and its impact on model
development. Overall, this research provides insights into YOLOv5's
capabilities and its position within the broader landscape of object detection
and why it is a popular choice for constrained edge deployment scenarios.
