Journal of Advanced Computational Intelligence and Intelligent Informatics
Online ISSN : 1883-8014
Print ISSN : 1343-0130
ISSN-L : 1883-8014
Regular Papers
Detection of Japanese Quails (Coturnix japonica) in Poultry Farms Using YOLOv5 and Detectron2 Faster R-CNN
Ivan Roy S. Evangelista Lenmar T. CatajayMaria Gemel B. PalconitMary Grace Ann C. BautistaRonnie S. Concepcion IIEdwin SybingcoArgel A. BandalaElmer P. Dadios
Author information
JOURNAL OPEN ACCESS

2022 Volume 26 Issue 6 Pages 930-936

Details
Abstract

Poultry, like quails, is sensitive to stressful environments. Too much stress can adversely affect birds’ health, causing meat quality, egg production, and reproduction to degrade. Posture and behavioral activities can be indicators of poultry wellness and health condition. Animal welfare is one of the aims of precision livestock farming. Computer vision, with its real-time, non-invasive, and accurate monitoring capability, and its ability to obtain a myriad of information, is best for livestock monitoring. This paper introduces a quail detection mechanism based on computer vision and deep learning using YOLOv5 and Detectron2 (Faster R-CNN) models. An RGB camera installed 3 ft above the quail cages was used for video recording. The annotation was done in MATLAB video labeler using the temporal interpolator algorithm. 898 ground truth images were extracted from the annotated videos. Augmentation of images by change of orientation, noise addition, manipulating hue, saturation, and brightness was performed in Roboflow. Training, validation, and testing of the models were done in Google Colab. The YOLOv5 and Detectron2 reached average precision (AP) of 85.07 and 67.15, respectively. Both models performed satisfactorily in detecting quails in different backgrounds and lighting conditions.

Content from these authors

This article cannot obtain the latest cited-by information.

© 2022 Fuji Technology Press Ltd.

This article is licensed under a Creative Commons [Attribution-NoDerivatives 4.0 International] license (https://creativecommons.org/licenses/by-nd/4.0/).
The journal is fully Open Access under Creative Commons licenses and all articles are free to access at JACIII official website.
https://www.fujipress.jp/jaciii/jc-about/#https://creativecommons.org/licenses/by-nd
Previous article Next article
feedback
Top