Summary of Comprehensive Performance Evaluation Of Yolov12, Yolo11, Yolov10, Yolov9 and Yolov8 on Detecting and Counting Fruitlet in Complex Orchard Environments, by Ranjan Sapkota et al.
Comprehensive Performance Evaluation of YOLOv12, YOLO11, YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments
by Ranjan Sapkota, Zhichao Meng, Martin Churuvija, Xiaoqiang Du, Zenghong Ma, Manoj Karkee
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study evaluates the performances of various YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12 object detection algorithms in detecting immature green apples (fruitlets) in commercial orchards. The evaluation metrics used include precision, recall, mean Average Precision at 50% Intersection over Union (mAP@50), and computational speeds. Among the configurations, YOLOv12l recorded the highest recall rate, while YOLOv10x achieved the highest precision score. The study also validates in-field counting of fruitlets using an iPhone and machine vision sensors, with YOLO11n demonstrating outstanding accuracy. Additionally, sensor-specific training on Intel Realsense data significantly enhanced model performance. The research underscores the suitability of YOLOv11n for real-time object detection applications due to its high inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study compares different ways of detecting immature green apples in orchards using a type of artificial intelligence called object detection algorithms. It tests various models, including YOLOv8, YOLOv9, and YOLOv12, to see which one works best. The researchers looked at how well each model detected the apples and how fast it could do so. They also compared the results with images taken by an iPhone camera. The study found that some models were better than others at detecting the apples and that training the models on special data improved their performance. |
Keywords
» Artificial intelligence » Inference » Mean average precision » Object detection » Precision » Recall
