Automatic Monitoring of Pigs` Physico-Temporal Activities at Different Greenhouse Gas Concentrations is a dataset for an object detection task. It is used in the livestock industry.

The dataset consists of 667 images with 3183 labeled objects belonging to 1 single class (pig).

Images in the Automatic Monitoring of Pigs dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. Every image has information about CO2_ppm and CO_NO_CH4_N2O_ppm gas concentrations, while every object has information about it’s posture: standing pig, sternal lying pig, lateral lying pig; and it’s treatment score and state: before treatment, during treatment, after treatment. To explore postures and treatments of every pig, run dataset in supervisely. The dataset was released in 2021 by the Gyeongsang National University, Korea and Ministry of Communication and Information Technology, Nepal.

Automatic Monitoring of Pigs` Physico-Temporal Activities at Different Greenhouse Gas Concentrations is under CC BY 4.0 license.

Source

If you make use of the Automatic Monitoring of Pigs data, please cite the following reference:

@Article{ani11113089,
  AUTHOR = {Bhujel, Anil and Arulmozhi, Elanchezhian and Moon, Byeong-Eun and Kim, Hyeon-Tae},
  TITLE = {Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations},
  JOURNAL = {Animals},
  VOLUME = {11},
  YEAR = {2021},
  NUMBER = {11},
  ARTICLE-NUMBER = {3089},
  URL = {https://www.mdpi.com/2076-2615/11/11/3089},
  PubMedID = {34827821},
  ISSN = {2076-2615},
  ABSTRACT = {Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs’ health and wellbeing. A poor farm environment increases the toxic GHGs, which might deteriorate pigs’ health and welfare. In this study a computer-vision-based automatic monitoring and tracking model was proposed to detect pigs’ short-term physical activities in the compromised environment. The ventilators of the livestock barn were closed for an hour, three times in a day (07:00–08:00, 13:00–14:00, and 20:00–21:00) to create a compromised environment, which increases the GHGs level significantly. The corresponding pig activities were observed before, during, and after an hour of the treatment. Two widely used object detection models (YOLOv4 and Faster R-CNN) were trained and compared their performances in terms of pig localization and posture detection. The YOLOv4, which outperformed the Faster R-CNN model, was coupled with a Deep-SORT tracking algorithm to detect and track the pig activities. The results revealed that the pigs became more inactive with the increase in GHG concentration, reducing their standing and walking activities. Moreover, the pigs shortened their sternal-lying posture, increasing the lateral lying posture duration at higher GHG concentration. The high detection accuracy (mAP: 98.67%) and tracking accuracy (MOTA: 93.86% and MOTP: 82.41%) signify the models’ efficacy in the monitoring and tracking of pigs’ physical activities non-invasively.},
  DOI = {10.3390/ani11113089}
}

Source

Dataset Automatic Monitoring of Pigs can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Automatic Monitoring of Pigs', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.