mlsanityData to 14 Oct 2026

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

arXiv 2504.13099 ↗First posted 17 Apr 2025cs.CVcited by 12

Not on the map. Why?

Abstract (arXiv)

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

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 28 of its 57 references that are in the map. First posted on arXiv 17 Apr 2025.

Builds on

28 of 57 references.

References · 28

  1. 2020End-to-End Object Detection with Transformerscited by 7,414
  2. 2017Mask R-CNNcited by 7,277
  3. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  4. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
  5. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  6. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  7. 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,319
  8. 2023DETRs Beat YOLOs on Real-time Object Detectioncited by 533
  9. 2015An Introduction to Convolutional Neural Networkscited by 514
  10. 2015Recent Advances in Convolutional Neural Networkscited by 360
  11. 2025YOLOv12: Attention-Centric Real-Time Object Detectorscited by 305
  12. 2022DETRs with Collaborative Hybrid Assignments Trainingcited by 274
16 more
  1. 2018MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devicescited by 182
  2. 2021You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detectioncited by 170
  3. 2017Learning non-maximum suppressioncited by 113
  4. 2023Contextual Object Detection with Multimodal Large Language Modelscited by 100
  5. 2023A survey of the Vision Transformers and their CNN-Transformer based Variantscited by 33
  6. 2020Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniquescited by 31
  7. 2022Attention Mechanism in Neural Networks: Where it Comes and Where it Goescited by 29
  8. 2019RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for freecited by 24
  9. 2024YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) seriescited by 22
  10. 2023Video OWL-ViT: Temporally-consistent open-world localization in videocited by 13
  11. 2023Immature Green Apple Detection and Sizing in Commercial Orchards using YOLOv8 and Shape Fitting Techniquescited by 9
  12. 2025Multimodal Large Language Models for Image, Text, and Speech Data Augmentation: A Surveycited by 7
  13. 2024Comparing YOLOv11 and YOLOv8 for instance segmentation of occluded and non-occluded immature green fruits in complex orchard environmentcited by 5
  14. 2024Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Developmentcited by 5
  15. 2024YOLO11 and Vision Transformers based 3D Pose Estimation of Immature Green Fruits in Commercial Apple Orchards for Robotic Thinningcited by 4
  16. 2025Improved YOLOv12 with LLM-Generated Synthetic Data for Enhanced Apple Detection and Benchmarking Against YOLOv11 and YOLOv10cited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 5.

12 papers.

  1. 2025YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detectioncited by 79
  2. 2025A Comparative Benchmark of Real-time Detectors for Blueberry Detection towards Precision Orchard Managementcited by 2
  3. 2025Container damage detection using advanced computer vision model Yolov12 vs Yolov11 vs RF-DETR A comparative analysiscited by 1
  4. 2025SynSpill: Improved Industrial Spill Detection With Synthetic Datacited by 1
  5. 2025Axis-Aligned 3D Stalk Diameter Estimation from RGB-D Imagerycited by 1
  6. 2026TennisExpert: Towards Expert-Level Analytical Sports Video Understandingcited by 1
  7. 2026Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challengecited by 1
  8. 2025Big Data, Tiny Targets: An Exploratory Study in Machine Learning-enhanced Detection of Microplastic from Filterscited by 0
  9. 2025Comparing Object Detection Models for Electrical Substation Component Mappingcited by 0
  10. 2026Multimodal Urban Tree Detection from Satellite and Street-Level Imagery via Annotation-Efficient Deep Learning Strategiescited by 0
  11. 2026FruitProM-V2: Robust Probabilistic Maturity Estimation and Detection of Fruits and Vegetablescited by 0
  12. 2026Training with synthetic data for drone detection in thermal imagerycited by 0