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
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
6807aa5868e39b28126fa252
Ranjan Sapkota
claimed_verified
2025-04-23T08:37:03.555Z
67ddd80896ac367438d400a6
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/C1NY6Nv5i0erwLnzCrTUM.png
Ranjan Sapkota
user
RanjanSapkota
6807aa5868e39b28126fa253
Rahul Harsha Cheppally
claimed_verified
2025-04-24T09:11:14.914Z
6328f8ac84e4c8b42c5d61b8
/avatars/7530f03d4359919d40af71beca374ce4.svg
Rahul Harsha Cheppally
user
r4hul77
6807aa5868e39b28126fa254
Ajay Sharda
6807aa5868e39b28126fa255
Manoj Karkee
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
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
RF-DETR outperforms YOLOv12 in detecting greenfruits in complex orchard environments with label ambiguity, occlusions, and background blending, particularly in multi-class scenarios.
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