title: 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
summary: 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
paper_authors._id: 6807aa5868e39b28126fa252
paper_authors.name: Ranjan Sapkota
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-23T08:37:03.555Z
paper_authors._id: 67ddd80896ac367438d400a6
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/C1NY6Nv5i0erwLnzCrTUM.png
paper_authors.fullname: Ranjan Sapkota
paper_authors.type: user
paper_authors.user: RanjanSapkota
paper_authors._id: 6807aa5868e39b28126fa253
paper_authors.name: Rahul Harsha Cheppally
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-24T09:11:14.914Z
paper_authors._id: 6328f8ac84e4c8b42c5d61b8
paper_authors.avatarUrl: /avatars/7530f03d4359919d40af71beca374ce4.svg
paper_authors.fullname: Rahul Harsha Cheppally
paper_authors.type: user
paper_authors.user: r4hul77
paper_authors._id: 6807aa5868e39b28126fa254
paper_authors.name: Ajay Sharda
paper_authors._id: 6807aa5868e39b28126fa255
paper_authors.name: Manoj Karkee
paper_title: 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
paper_summary: 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
paper_ai_summary: RF-DETR outperforms YOLOv12 in detecting greenfruits in complex orchard environments with label ambiguity, occlusions, and background blending, particularly in multi-class scenarios.
paper_ai_keywords: RF-DETR YOLOv12 DINOv2 deformable attention CNN-based attention mean Average Precision (mAP50) mAP@50:95 single-class detection multi-class detection training dynamics transformer-based architectures