A Comparative Study of Modern Object Detectors for Robust Apple Detection in Orchard Imagery

• URL: http://arxiv.org/abs/2604.09996v1
• Date: Sat, 11 Apr 2026 02:56:42 GMT
• Title: A Comparative Study of Modern Object Detectors for Robust Apple Detection in Orchard Imagery
• Abstract summary: This study establishes a benchmark for single-class apple detection on the public AppleBBCH81 dataset.<n>On the validation split, YOLOv10n achieves the best strict localization performance.<n>YOLOv10n attains the highest F1-score, whereas RT-DETR-L achieves very high recall but low precision.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate apple detection in orchard images is important for yield prediction, fruit counting, robotic harvesting, and crop monitoring. However, changing illumination, leaf clutter, dense fruit clusters, and partial occlusion make detection difficult. To provide a fair and reproducible comparison, this study establishes a controlled benchmark for single-class apple detection on the public AppleBBCH81 dataset using one deterministic train, validation, and test split and a unified evaluation protocol across six representative detectors: YOLOv10n, YOLO11n, RT-DETR-L, Faster R-CNN (ResNet50-FPN), FCOS (ResNet50-FPN), and SSDLite320 (MobileNetV3-Large). Performance is evaluated primarily using COCO-style mAP@0.5 and mAP@0.5:0.95, and threshold-dependent behavior is further analyzed using precision-recall curves and fixed-threshold precision, recall, and F1-score at IoU = 0.5. On the validation split, YOLO11n achieves the best strict localization performance with mAP@0.5:0.95 = 0.6065 and mAP@0.5 = 0.9620, followed closely by RT-DETR-L and YOLOv10n. At a fixed operating point with confidence >= 0.05, YOLOv10n attains the highest F1-score, whereas RT-DETR-L achieves very high recall but low precision because of many false positives at low confidence. These findings show that detector selection for orchard deployment should be guided not only by localization-aware accuracy but also by threshold robustness and the requirements of the downstream task.

Related papers

• A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa [0.0]
  This study presents a comparative evaluation of six object detection models YOLOv5, YOLOv8, YOLO11, YOLO26, Faster R-CNN, and RT-DETR.<n>The results show that RT-DETR achieved the highest overall performance with a precision of 0.768 and mAP@0.5:0.95 of 0.624.<n>YOLO-based models exhibited superior training efficiency compared to Faster R-CNN.
  arXiv  Detail & Related papers  (2026-08-11T15:22:58Z)
• LLM-Driven Neural Network Generation with Same-Family Architecture Guidance: Disentangling Transfer and Adaptation [48.83701310501069]
  Large language models (LLMs) can generate neural-network modifications, but unrestricted generation is often invalid or harmful.<n>This paper studies a narrower setting: improving a weak target model using a stronger same-family source model from a neural-network database.<n>We propose a source-guided candidate-generation protocol with non-source controls, source-conditioned candidates, and a no-LLM hp_copy ablation under equal evaluation budgets.
  arXiv  Detail & Related papers  (2026-07-06T23:59:18Z)
• BloomNet: Exploring Single vs. Multiple Object Annotation for Flower Recognition Using YOLO Variants [0.0]
  This paper benchmarks several YOLO architectures such as YOLOv5s, YOLOv8n/s/m, and YOLOv12n for object detection under two annotation regimes.<n>The FloralSix dataset, comprising 2,816 high-resolution photos of six different flower species, is also introduced.
  arXiv  Detail & Related papers  (2026-02-20T19:47:45Z)
• CVeDRL: An Efficient Code Verifier via Difficulty-aware Reinforcement Learning [57.24524263804788]
  Code verifiers play a critical role in post-verification for LLM-based code generation.<n>Existing supervised fine-tuning methods suffer from data scarcity, high failure rates, and poor inference efficiency.<n>We show that naive RL with only functionality rewards fails to generate effective unit tests for difficult branches and samples.
  arXiv  Detail & Related papers  (2026-01-30T10:33:29Z)
• N-EIoU-YOLOv9: A Signal-Aware Bounding Box Regression Loss for Lightweight Mobile Detection of Rice Leaf Diseases [0.6280530476948474]
  We propose N EIoU YOLOv9, a lightweight detection framework based on a signal aware bounding box regression loss.<n>The proposed loss reshapes localization gradient by combining non monotonic focusing with decoupled width and height optimization.<n>This design is particularly effective for small and low contrast targets commonly observed in agricultural disease imagery.
  arXiv  Detail & Related papers  (2026-01-14T05:13:36Z)
• Vision-Based Driver Drowsiness Monitoring: Comparative Analysis of YOLOv5-v11 Models [0.0]
  Driver drowsiness remains a critical factor in road accidents, accounting for thousands of fatalities and injuries each year.<n>This paper presents a comprehensive evaluation of real-time, non-intrusive drowsiness detection methods, focusing on computer vision based YOLO algorithms.
  arXiv  Detail & Related papers  (2025-09-22T08:30:02Z)
• Impact of Labeling Inaccuracy and Image Noise on Tooth Segmentation in Panoramic Radiographs using Federated, Centralized and Local Learning [46.232038247686745]
  Federated learning (FL) may mitigate privacy constraints, heterogeneous data quality, and inconsistent labeling in dental diagnostic AI.<n>We compared FL with centralized (CL) and local learning (LL) for tooth segmentation in panoramic radiographs across multiple data corruption scenarios.
  arXiv  Detail & Related papers  (2025-09-08T11:07:47Z)
• A Lightweight and Robust Framework for Real-Time Colorectal Polyp Detection Using LOF-Based Preprocessing and YOLO-v11n [2.4896276458398785]
  This study introduces a new, lightweight, and efficient framework for polyp detection.<n>It combines the Local Outlier Factor algorithm for filtering noisy data with the YOLO-v11n deep learning model.<n>Compared to previous YOLO-based methods, our model demonstrates enhanced accuracy and efficiency.
  arXiv  Detail & Related papers  (2025-07-14T23:36:54Z)
• 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 [0.8488322025656239]
  This study conducts a detailed comparison of RF-DETR object detection base model and YOLOv12 object detection model configurations.<n>A custom dataset was developed featuring both single-class (greenfruit) and multi-class (occluded and non-occluded greenfruits) annotations.<n>RF-DETR model, utilizing a DINOv2 backbone and deformable attention, excelled in global context modeling.<n>YOLOv12 leveraged CNN-based attention for enhanced local feature extraction, optimizing it for computational efficiency and edge deployment.
  arXiv  Detail & Related papers  (2025-04-17T17:08:11Z)
• Robust Fine-tuning of Zero-shot Models via Variance Reduction [56.360865951192324]
  When fine-tuning zero-shot models, our desideratum is for the fine-tuned model to excel in both in-distribution (ID) and out-of-distribution (OOD) We propose a sample-wise ensembling technique that can simultaneously attain the best ID and OOD accuracy without the trade-offs.
  arXiv  Detail & Related papers  (2024-11-11T13:13:39Z)
• Guided Score identity Distillation for Data-Free One-Step Text-to-Image Generation [62.30570286073223]
  Diffusion-based text-to-image generation models have demonstrated the ability to produce images aligned with textual descriptions.<n>We introduce a data-free guided distillation method that enables the efficient distillation of pretrained Diffusion models without access to the real training data.<n>By exclusively training with synthetic images generated by its one-step generator, our data-free distillation method rapidly improves FID and CLIP scores, achieving state-of-the-art FID performance while maintaining a competitive CLIP score.
  arXiv  Detail & Related papers  (2024-06-03T17:44:11Z)
• Towards General Robustness Verification of MaxPool-based Convolutional Neural Networks via Tightening Linear Approximation [51.235583545740674]
  MaxLin is a robustness verifier for MaxPool-based CNNs with tight linear approximation. We evaluate MaxLin with open-sourced benchmarks, including LeNet and networks trained on the MNIST, CIFAR-10, and Tiny ImageNet datasets.
  arXiv  Detail & Related papers  (2024-06-02T10:33:04Z)
• Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy [42.15969584135412]
  Neural network pruning is a popular technique used to reduce the inference costs of modern networks. We evaluate whether the use of test accuracy alone in the terminating condition is sufficient to ensure that the resulting model performs well. We find that pruned networks effectively approximate the unpruned model, however, the prune ratio at which pruned networks achieve commensurate performance varies significantly across tasks.
  arXiv  Detail & Related papers  (2021-03-04T13:22:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.