对比两种模型在模糊果园中检测青果的性能，发现Transformer模型更准，CNN模型更快。
RF-DETR Object Detection vs YOLOv12 : A Study of Transformer-based and CNN-based Architectures for Single-Class and Multi-Class Greenfruit Detection in Complex Orchard Environments Under Label Ambiguity
- 用DINOv2+可变形注意力的RF-DETR捕捉全局上下文，适合遮挡场景
- RF-DETR单类检测mAP50达0.9464，多类检测达0.8298，精度领先
- YOLOv12计算效率高，适合边缘部署，适合快速响应需求
本研究对比了基于Transformer的RF-DETR与基于CNN的YOLOv12模型在复杂果园环境下检测青果的表现，该环境存在标签模糊、遮挡和背景融合问题。构建了包含单类（青果）和多类（遮挡与非遮挡青果）标注的自定义数据集，评估模型在动态真实条件下的性能。RF-DETR采用DINOv2骨干网络与可变形注意力机制，在全局上下文建模上表现优异，有效识别部分遮挡或模糊的青果。相比之下，YOLOv12利用基于CNN的注意力机制，强化局部特征提取，优化计算效率，适合边缘部署。在单类检测中，RF-DETR达到最高mAP50为0.9464，证明其在杂乱场景中的定位能力。尽管YOLOv12N在mAP@50:95上取得0.7620的最高值，但RF-DETR在复杂空间场景中持续领先。多类检测中，RF-DETR以0.8298的mAP@50领先，能有效区分遮挡与非遮挡果实；而YOLOv12L在mAP@50:95上达0.6622，表明其在详细遮挡情境下分类更优。训练动态分析显示，RF-DETR收敛迅速，单类设置下10个周期内即趋于稳定，体现其对动态视觉数据的适应效率。结果验证了RF-DETR在精准农业中的有效性，而YOLOv12适用于快速响应场景。
原文摘要 · Abstract (English)
This study conducts a detailed comparison of RF-DETR object detection base model and YOLOv12 object detection model configurations for detecting greenfruits in a complex orchard environment marked by label ambiguity, occlusions, and background blending. A custom dataset was developed featuring both single-class (greenfruit) and multi-class (occluded and non-occluded greenfruits) annotations to assess model performance under dynamic real-world conditions. RF-DETR object detection model, utilizing a DINOv2 backbone and deformable attention, excelled in global context modeling, effectively identifying partially occluded or ambiguous greenfruits. In contrast, YOLOv12 leveraged CNN-based attention for enhanced local feature extraction, optimizing it for computational efficiency and edge deployment. RF-DETR achieved the highest mean Average Precision (mAP50) of 0.9464 in single-class detection, proving its superior ability to localize greenfruits in cluttered scenes. Although YOLOv12N recorded the highest mAP@50:95 of 0.7620, RF-DETR consistently outperformed in complex spatial scenarios. For multi-class detection, RF-DETR led with an mAP@50 of 0.8298, showing its capability to differentiate between occluded and non-occluded fruits, while YOLOv12L scored highest in mAP@50:95 with 0.6622, indicating better classification in detailed occlusion contexts. Training dynamics analysis highlighted RF-DETR's swift convergence, particularly in single-class settings where it plateaued within 10 epochs, demonstrating the efficiency of transformer-based architectures in adapting to dynamic visual data. These findings validate RF-DETR's effectiveness for precision agricultural applications, with YOLOv12 suited for fast-response scenarios. >Index Terms: RF-DETR object detection, YOLOv12, YOLOv13, YOLOv14, YOLOv15, YOLOE, YOLO World, YOLO, You Only Look Once, Roboflow, Detection Transformers, CNNs
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