# dataset

**Repository Path**: rubin81/dataset

## Basic Information

- **Project Name**: dataset
- **Description**: 奶山羊多视觉任务数据集DairyGoatMVT
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-04-01
- **Last Updated**: 2024-04-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## DiaryGoatMVT
Welcome to our DiaryGoatMVT! This repository contains a variety of annotated datasets for visual tasks created by our lab, including id identificatioin, instance segmentation, object detection, object tracking, pose estimation, behavior recognition, semantic segmentation, and image generation. Our goal is to advance the field of intelligent agriculture and provide data and references for future researchers. 

我们首次公开、共享了奶山羊多视觉任务数据集DairyGoatMVT，该数据集是我们团队十多年积累的成果，主要面向奶山羊的目标检测、目标跟踪、姿态估计、行为识别、个体识别、语义分割、实例分割及图像生成等多视觉任务。欢迎各位老师批评指正，后续我们将会继续更新、完善和共享相关数据，以期为畜禽视觉任务模型、农业大模型提供数据支撑。

## Data Download
You can download all the datasets through the [Link](https://pan.baidu.com/s/1rRBElmpcb7O6RaG5X8GNrw?pwd=3vgb).
## Addtional Information
团队其他相关成果：

(1) DGAnnotation交互式标注系统[下载](https://pan.baidu.com/s/1PAD-9-e8lb7PJUtFdYyhfg?pwd=yi7k)：基于DeepLabv3+研发的交互式标注系统，其标注速度为Labelme的5倍，可实现奶山羊像素级语义分割。

(2) 羊病辅助诊断与学习平台：结合Trans-CNN和知识图谱实现羊病辅助诊断，并提供专业学习资料及羊友论坛。平台二维码如下：
<p align="center">
  <img src="pics/QR_code.jpeg" width="200"/>
</p>


This dataset is free for academic usage. For other purposes, please contact us (tangjinglei@nwsuaf.edu.cn).

If you find our work useful in your research, please cite:
  - ### Sgementation:
```
@article{zhang2023interactive,
  title={Interactive Dairy Goat Image Segmentation for Precision Livestock Farming},
  author={Zhang, Lianyue and Han, Gaoge and Qiao, Yongliang and Xu, Liu and Chen, Ling and Tang, Jinglei},
  journal={Animals},
  volume={13},
  number={20},
  pages={3250},
  year={2023},
  publisher={MDPI}
}
```
- ### Object Detection:
```
@article{tang2019salient,
  title={Salient object detection of dairy goats in farm image based on background and foreground priors},
  author={Tang, Jinglei and Yang, Guoxin and Sun, Yurou and Xin, Jing and He, Dongjian},
  journal={Neurocomputing},
  volume={332},
  pages={270--282},
  year={2019},
  publisher={Elsevier}
}
```
```
@article{wang2018dairy,
  title={Dairy goat detection based on Faster R-CNN from surveillance video},
  author={Wang, Dong and Tang, JingLei and Zhu, Weijie and Li, Huan and Xin, Jing and He, Dongjian},
  journal={Computers and Electronics in Agriculture},
  volume={154},
  pages={443--449},
  year={2018},
  publisher={Elsevier}
}
```
- ### Object Tracking:
```
@article{su2021automatic,
  title={Automatic tracking of the dairy goat in the surveillance video},
  author={Su, Qingguo and Tang, Jinglei and Zhai, Jinhui and Sun, Yurou and He, Dongjian},
  journal={Computers and Electronics in Agriculture},
  volume={187},
  pages={106254},
  year={2021},
  publisher={Elsevier}
}
```
```
@article{su2022intelligent,
  title={An intelligent method for dairy goat tracking based on Siamese network},
  author={Su, Qingguo and Tang, Jinglei and Zhai, Mingxin and He, Dongjian},
  journal={Computers and Electronics in Agriculture},
  volume={193},
  pages={106636},
  year={2022},
  publisher={Elsevier}
}
```
- ### Pose Estimation & Behavior Recognition:
```
@article{chen2024grmpose,
  title={GRMPose: GCN-based real-time dairy goat pose estimation},
  author={Chen, Ling and Zhang, Lianyue and Tang, Jinglei and Tang, Chao and An, Rui and Han, Ruizi and Zhang, Yiyang},
  journal={Computers and Electronics in Agriculture},
  volume={218},
  pages={108662},
  year={2024},
  publisher={Elsevier}
}
```
- ### Image Generation
```
@article{li2020dairy,
  title={Dairy goat image generation based on improved-self-attention generative adversarial networks},
  author={Li, Huan and Tang, Jinglei},
  journal={IEEE Access},
  volume={8},
  pages={62448--62457},
  year={2020},
  publisher={IEEE}
}
```