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Detecting Sword using YOLO Algorithm for Surveillance System

Sayed, Mehazabin Younus (2023) Detecting Sword using YOLO Algorithm for Surveillance System. Masters thesis, Dublin, National College of Ireland.

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In the current circumstance, identifying the hidden tools or objects beneath people’s clothes is a challenging task also, domestic violence against women in Ireland is a significant issue, with one in five women experiencing physical or sexual violence in their lifetime. In 2020, the Irish police service, An Garda Síochána, reported a total of 15,717 incidents of domestic violence, a 10 percent increase from 2019. However, avoiding it can pose security concerns and be a life-threatening risk to individuals. Due to the current situation around the world, automated visual surveillance is essential for security to detect swords, it is the first algorithm to achieve high accuracy and speed in object detection. In this circumstance, this paper aims to explain the effectiveness of YOLO in identifying and detecting objects like swords. The study has reviewed the two variants of YOLO, specifically, YoloV3 and YoloV5, to identify the better algorithm for detecting swords. In this consideration, the study has reviewed several pieces of literature and adopted various required methods to examine the objectives and answer the research questions effectively. The research has critically analyzed the results and discussed and come up with the view that YoloV3 is the most effective algorithm in comparison to YoloV5 to detect swords.

Item Type: Thesis (Masters)
Anant, Aaloka
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms
H Social Sciences > HQ The family. Marriage. Woman > Domestic Violence
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 25 May 2023 16:19
Last Modified: 25 May 2023 16:19

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