  This paper presents a comprehensive review of the evolution of the YOLO (You
Only Look Once) object detection algorithm, focusing on YOLOv5, YOLOv8, and
YOLOv10. We analyze the architectural advancements, performance improvements,
and suitability for edge deployment across these versions. YOLOv5 introduced
significant innovations such as the CSPDarknet backbone and Mosaic
Augmentation, balancing speed and accuracy. YOLOv8 built upon this foundation
with enhanced feature extraction and anchor-free detection, improving
versatility and performance. YOLOv10 represents a leap forward with NMS-free
training, spatial-channel decoupled downsampling, and large-kernel
convolutions, achieving state-of-the-art performance with reduced computational
overhead. Our findings highlight the progressive enhancements in accuracy,
efficiency, and real-time performance, particularly emphasizing their
applicability in resource-constrained environments. This review provides
insights into the trade-offs between model complexity and detection accuracy,
offering guidance for selecting the most appropriate YOLO version for specific
edge computing applications.
