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.

翻译：本文对YOLO（You Only Look Once）目标检测算法的演进历程进行了全面综述，重点聚焦于YOLOv5、YOLOv8和YOLOv10三个版本。我们分析了这些版本在架构创新、性能提升以及边缘部署适用性方面的进展。YOLOv5引入了CSPDarknet主干网络和马赛克数据增强等重大创新，在速度与精度间取得了平衡。YOLOv8在此基础上通过增强特征提取能力和采用无锚框检测机制，提升了模型的通用性与性能。YOLOv10实现了跨越式发展，采用无需非极大值抑制的训练策略、空间-通道解耦下采样以及大核卷积技术，在降低计算开销的同时达到了最先进的性能水平。我们的研究结果凸显了该系列算法在精度、效率与实时性能方面的持续进步，特别强调了其在资源受限环境中的适用性。本综述深入探讨了模型复杂度与检测精度之间的权衡关系，为特定边缘计算应用场景中选择最合适的YOLO版本提供了指导。