对比YOLO各版本在不同硬件上的小目标检测表现
Small Object Detection with YOLO: A Performance Analysis Across Model Versions and Hardware
- 测试YOLOv5至v11在CPU/GPU上推理速度与精度
- 发现小目标（占图像1%-5%）检测性能随版本提升而改善
- 适合部署于嵌入式设备或边缘计算的开发者参考
本研究系统评估了从YOLOv5到YOLOv11的多个版本在不同硬件平台和优化库下的表现。实验覆盖Intel和AMD CPU，使用ONNX与OpenVINO；以及通过TensorRT等框架在GPU上的部署。特别关注模型对小目标的敏感性，分析其在目标仅占图像1%、2.5%和5%面积时的检测性能。结果揭示各版本在效率、精度与小目标适应性之间的权衡，为基于特定硬件限制和检测需求选择最优模型提供依据，助力实际应用中的有效部署。
原文摘要 · Abstract (English)
This paper provides an extensive evaluation of YOLO object detection models (v5, v8, v9, v10, v11) by com- paring their performance across various hardware platforms and optimization libraries. Our study investigates inference speed and detection accuracy on Intel and AMD CPUs using popular libraries such as ONNX and OpenVINO, as well as on GPUs through TensorRT and other GPU-optimized frameworks. Furthermore, we analyze the sensitivity of these YOLO models to object size within the image, examining performance when detecting objects that occupy 1%, 2.5%, and 5% of the total area of the image. By identifying the trade-offs in efficiency, accuracy, and object size adaptability, this paper offers insights for optimal model selection based on specific hardware constraints and detection requirements, aiding practitioners in deploying YOLO models effectively for real-world applications.
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