Computer Vision for Weapon Detection in Educational Environments: A Systematic Literature Review
Year: 2024
Type: review
Study design: Systematic Review
Abstract: This study presents a systematic review of the literature on the use of computer vision algorithms for weapon detection in educational environments. Through the analysis of 13 selected studies from an initial corpus of 10,519 articles, the results demonstrate that models based on Convolutional Neural Networks, particularly variants of YOLO, are predominantly used due to their high accuracy and real-time efficiency. This work highlights the need for technological advancements to address challenges such as the variability of weapon types and the diverse school scenarios. Furthermore, the practical implications of these technologies in enhancing school security and the importance of ethical and privacy considerations are discussed. The review also reveals significant gaps in current research, such as the lack of studies focused on specific educational environments and the need for more representative and diverse datasets.
Authors: Maurício Rodrigues Lima, Deller James Ferreira, Elisângela Silva Dias, Marcos Reges Mota, Ana Luísa de Bastos Chagas, Pedro Lemes Sixel Lobo
Institution: Universidade Federal de Goiás
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4403388922