These codes and datsets are from the paper "Growing status observation for oil palm tree using Unmanned Aerial Vehicle (UAV) images", which is published in ISPRS Photogrammetry and Remote Sensing.

CUDA_VISIBLE_DEVICES=gpu_id python tools/train.py configs/oilPalmUav/mopad.py

CUDA_VISIBLE_DEVICES=gpu_id python demo/demoFull.py configs/oilPalmUav/mopad.py work_dirs/mopad/latest.pth mopad-det.txt test_images

Our training models for Site 2 can be downloaded from

Baidu Wangpan Access: 7n61

Our training models for Site 1 can be downloaded from

Baidu Wangpan Access: 8mwa

Our dataset for Site 2 can be downloaded from

Google Drive

Baidu Wangpan Access: qpaw

Our dataset for Site 1 can be downloaded from

Baidu Wangpan Access: fgfv

The data should be saved in the folder ./data

We followed COCO format basically.

The structure of the dataset is as follows:

• train2017: images for training dataset (like <id>.jpg)
• val2017: images for validation dataset (like <id>.jpg)
• annotations: annotations including instances_train2017.json and instances_val2017.json for training and validation dataset, respectively

If you use this code for your research, please consider citing:

@article{zheng2021growing,
  title={Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images},
  author={Zheng, Juepeng and Fu, Haohuan and Li, Weijia and Wu, Wenzhao and Yu, Le and Yuan, Shuai and Tao, Wai Yuk William and Pang, Tan Kian and Kanniah, Kasturi Devi},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  volume={173},
  pages={95--121},
  year={2021},
  publisher={Elsevier}
}

Zheng, J., Fu, H., Li, W., Wu, W., Yu, L., Yuan, S., ... & Kanniah, K. D. (2021). Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 95-121.

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