TailSafe: A Pig Head-to-Rear Contact Detection System using Convolutional Neural Networks

dc.contributor.author	Santos, Romwell Joackin O.
dc.date.accessioned	2024-05-14T23:35:49Z
dc.date.available	2024-05-14T23:35:49Z
dc.date.issued	2023-06
dc.identifier.uri	http://dspace.cas.upm.edu.ph:8080/xmlui/handle/123456789/2697
dc.description.abstract	Pig tail biting poses significant challenges in pig farm monitoring, serving as an indicator of underlying pen issues. Existing monitoring methods are limited in their scalability and invasiveness. This study introduces TailSafe, a web-based decision support tool utilizing YOLOv5 and convolutional neural networks. TailSafe enables farmers to diagnose pig pen issues through potential tail biting outbreaks. Users upload pig pen images for processing, and the system provides results for contact presence classification and counts. TailSafe comprises two components: a detection method to identify pig heads and rears, and an interaction method to compute head-to-rear IoUs for contact identification.	en_US
dc.subject	Tail biting	en_US
dc.subject	Decision support tool	en_US
dc.subject	YOLOv5	en_US
dc.subject	Convolutional neural network	en_US
dc.subject	Pig pen images	en_US
dc.subject	Contact presence	en_US
dc.subject	Detection method	en_US
dc.subject	Interaction method	en_US
dc.title	TailSafe: A Pig Head-to-Rear Contact Detection System using Convolutional Neural Networks	en_US
dc.type	Thesis	en_US