Authors:
Charith Gunasekara
1
;
Yash Matharu
2
and
Rohan Ben Joseph
3
Affiliations:
1
Department of National Defence, Government of Canada, Ottawa, ON, Canada
;
2
Faculty of Engineering, McMaster University, Hamilton, ON, Canada
;
3
Department of Computing Science, Simon Fraser University, Burnaby, BC, Canada
Keyword(s):
Computer Vision, Object Detection, Document Classification, YOLOv5.
Abstract:
This paper outlines a robust approach to automate the detection of military badges on official government documents utilizing YOLOv5 computer vision model. In an era where the rapid classification and management of sensitive documents is paramount, developing a system capable of accurately identifying and classifying distinct badge types plays a crucial role in supporting data management and security protocols. To address the challenges posed by the lack of accessible, real-world government and military documents for research, we introduced a novel method to simulate training data. We employ a technique that automates the data labelling process, facilitating the generation of a comprehensive and versatile dataset while eliminating the risk of compromising sensitive information. Through careful model training and hyper-parameter tuning, the YOLOv5 model demonstrated exemplary performance, successfully detecting a wide spectrum of badge types across various documents.