Document Type
Dissertation
Rights
This item is available under a Creative Commons License for non-commercial use only
Disciplines
1.2 COMPUTER AND INFORMATION SCIENCE, Computer Sciences, Information Science
Abstract
Maintaining the cattle farm along with the wellbeing of every heifer has been the major concern in dairy farm. A robust system is required which can tackle the problem of continuous monitoring of cows. the computer vision techniques provide a new way to understand the challenges related to the identification and welfare of the cows. This paper presents a state-of-art instance segmentation mask RCNN algorithm to train and build a model on a very challenging cow dataset that is captured during the winter season. The dataset poses many challenges such as overlapping of cows, partial occlusion, similarity between cows and background, and bad lightening. An attempt is made to improve the accuracy of the segmenter and the performance is measured after fine tuning the baseline model. The experiment result shows that fine tuning the mask RCNN algorithm helps in significantly improving the accuracy of instance segmentation of cows. this work is a contribution towards the real time monitoring of cows in cattle farm environment with the purpose of behavioural analysis of the cattle.
DOI
https://doi.org/10.21427/D7S51F
Recommended Citation
Danish, M. (2018). Beef Cattle Instance Segmentation Using Mask R-Convolutional Neural Network. Dissertation M.Sc. in Computing (Data Analytics), DIT, 2018.
Publication Details
A dissertation submitted in the partial fulfilment of the requirements of Technological University Dublin for the degree of M.Sc. in Computing (Data Analytics)