Deer Surveillance System in Public Parks Using Deep Learning

Cardenas Rodríguez, Mardwin Alejandro (2022) Deer Surveillance System in Public Parks Using Deep Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

The importance of disease transmission between wild animals and people is one of the most serious issue while people trying to get too close to wildlife. This research developed a method for detecting persons who are not following park regulations by utilizing deep learning approaches to detect people and deers in photos or videos. The suggested system used a YOLOv5 object detection algorithm to detect humans approaching deer too closely. The presented method was designed to be used in a drone video, and the system was well-trained to spot people and deer on camera or videos. The most significant limitation of this study was the lack of drone videos from the Phoenix park, as flying a drone is not permitted. We could not turn the video into a top-down image for distance estimation in the 2-D area because there were no camera specifications to do it, Despite the limitations mentioned above, the model was effectively trained to recognize people and deer, as well as to provide a document with the labels and the coordinates of the object detected in the image for further processing.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	YOLOv5; Deep Learning; Surveillance System; Object Recognition
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QL Zoology Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	19 Jan 2023 15:33
Last Modified:	06 Mar 2023 15:44
URI:	https://norma.ncirl.ie/id/eprint/6095

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