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VHRTrees

This repository contains weights for YOLOv5, YOLOv7, YOLOv8, and YOLOv9 networks trained with the VHRTrees dataset.

Dataset Details

The imagery required for the dataset has been obtained from Google Earth. We have downloaded 218 RGB images, each with a resolution of 1920x1080 pixels and a ground sample distance (GSD) of 0.5 km, covering the districts of Karacabey in Bursa, Turkey and Dikili, Aliağa, Seferihisar, and Selçuk in İzmir, Turkey. A sample image and corresponding minimum boxes for trees can be seen the figure. The dataset has been approximately split as 70% (1023 images), 15% (226 images) and 15% (222 images) for training, validation and testing, respectively.

sample_2_github1

Models, Metric Results and Weights

Experiments were conducted on Kaggle P100, Colab A100, and NVIDIA Quadro P6000 GPUs. All experiments were conducted on powerful GPUs and executed concurrently, resulting in similar durations. The evaluation results of the top-performing eight experiments are presented in the table below.

Experiment No Network Optimizer Model Batch Size Epoch F-1 Score Precision Recall mAP50 mAP50-95 Link
Exp-1 960x960 Auto YOLOv8m 16 50 0.932 0.932 0.932 0.934 0.608 Download
Exp-2 960x960 SGD YOLOv8m 16 50 0.915 0.942 0.890 0.916 0.608 Download
Exp-3 640x640 SGD YOLOv9-gelan-c 8 45 0.928 0.924 0.932 0.936 0.594 Download
Exp-4 640x640 SGD YOLOv9-gelan-c 8 45 0.924 0.922 0.927 0.534 0.584 Download
Exp-5 960x960 SGD YOLOv5m 16 50 0.929 0.932 0.926 0.934 0.569 Download
Exp-6 960x960 SGD YOLOv5s 16 50 0.931 0.930 0.933 0.933 0.567 Download
Exp-7 960x960 SGD YOLOv7-X 16 50 0.930 0.923 0.937 0.912 0.552 Download
Exp-8 960x960 SGD YOLOv7 16 50 0.929 0.930 0.928 0.908 0.549 Download

Visual Results

figure3_visual_results

Citation:

Please kindly cite our paper if this code and the dataset used in the study are useful for your research.

Topgül, Ş. N., Sertel, E., Aksoy, S., Ünsalan, C., & Fransson, J. E. S. (2024). VHRTrees: A New Benchmark Dataset for Tree Detection in Satellite Imagery and Performance Evaluation with YOLO-based Models. Frontiers in Forests and Global Change, 7. doi.org/10.3389/ffgc.2024.1495544.