Visão Computacional: detecção de armas utilizando YOLO V8
Resumo
Este estudo examina a aplicação do YOLOv8 para a detecção de armas em ambientes sensíveis à segurança. Utilizando um conjunto de dados anotado contendo diversos tipos de armas de fogo em diferentes condições, foi implementada a configuração yolov8s para otimizar tanto a precisão da detecção quanto a eficiência computacional. Os resultados demonstram capacidades particularmente fortes de detecção para certas categorias de armas, mantendo um desempenho confiável em todas as classes. Esses achados sugerem um potencial significativo do YOLOv8 para aplicações de segurança, oferecendo melhorias em relação aos métodos anteriores tanto em precisão quanto em velocidade de processamento. O desempenho equilibrado do modelo o torna especialmente adequado para ambientes onde a identificação rápida é crucial.
Palavras-chave:
YoloV8, Detecção de Armas, Aprendizado Profundo
Referências
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F. A. Warsi, M. Abdullah, M. N. Husen, M. Yahya, S. Khan, and N. Jawaid, “Gun detection system using yolov3,” in 2019 IEEE 6th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA). Kuala Lumpur, Malaysia: IEEE, 2019.
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S. Pawar, K. Pandit, and N. N. Chiplunkar, “Fire and gun detection using yolo v3 for surveillance system,” in International Conference on Artificial Intelligence and Data Engineering (AIDE). IEEE, 2022.
N. N C, “Smart surveillance: Deep learning-based weapon detection using yolo,” International Research Journal of Innovations in Engineering and Technology, vol. 12, pp. 2395–0056, 03 2025.
Python Software Foundation, “Python: Linguagem de programação — versão 3.10.12,” [link], 2025, linguagem de programação interpretada, utilizada neste trabalho na versão 3.10.12.
S. Narejo, B. Pandey, D. Esenarro Vargas, C. Rodriguez, and M. R. Anjum, “Weapon detection using yolo v3 for smart surveillance system,” Mathematical Problems in Engineering, vol. 2021, no. 1, p. 9975700, 2021.
United Nations, Office on Drugs and Crime, “Global study of homicide,” 2025, accessed: March 24, 2025. [Online]. Available: [link]
S. Velastin, B. Boghossian, and M. Vicencio-Silva, “A motion-based image processing system for detecting potentially dangerous situations in underground railway stations,” Transportation Research Part C: Emerging Technologies, vol. 14, pp. 96–113, 04 2006.
T. T. Santos, J. G. A. Barbedo, S. Ternes, J. Camargo Neto, L. V. Koenigkan, and K. X. S. d. Souza, “Visão computacional aplicada na agricultura,” Agricultura digital: pesquisa, desenvolvimento e inovação nas cadeias produtivas, pp. 146–164, 2020.
G. Jocher and U. Team, “Ultralytics yolo11: Cutting-edge object detection,” 2025, accessed: March 24, 2025. [Online]. Available: [link]
M. Hussain, “Yolov1 to v8: Unveiling each variant–a comprehensive review of yolo,” IEEE access, vol. 12, pp. 42 816–42 833, 2024.
M. Lima, D. Ferreira, E. Dias, M. Mota, A. Chagas, and P. Lobo, “Computer vision for weapon detection in educational environments: A systematic literature review,” in Anais Estendidos do XXX Simpósio Brasileiro de Sistemas Multimídia e Web. Porto Alegre, RS, Brasil: SBC, 2024, pp. 121–128. [Online]. Available: [link]
N. Anand and R. K. Koshariya, “SaveLives - A Real-Time Threat Detection System,” in 2022 1st International Conference on Informatics (ICI). Noida, India: IEEE, Apr. 2022, pp. 114–119. [Online]. Available: [link]
S. Ahmed, M. T. Bhatti, M. G. Khan, B. Lövström, and M. Shahid, “Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos,” Applied Sciences, vol. 12, no. 12, p. 5772, Jun. 2022. [Online]. Available: [link]
P. Isaac Ritharson, G. Madhavan, M. Rajeswari, and D. Brindha, “Prevention of School Shooting using Neural Networks and Computer Vision,” in 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT). Kannur, India: IEEE, Aug. 2022, pp. 1703–1709. [Online]. Available: [link]
J. Ruiz-Santaquiteria, A. Velasco-Mata, N. Vallez, O. Deniz, and G. Bueno, “Improving handgun detection through a combination of visual features and body pose-based data,” Pattern Recognition, vol. 136, p. 109252, Apr. 2023. [Online]. Available: [link]
S. M. Keerthana, S. R., and Y. P., “Weapon detection for security using the yolo algorithm with email alert notification,” in 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET). IEEE, 2024.
P. Yadav, N. Gupta, and P. K. Sharma, “Robust weapon detection in dark environments using Yolov7-DarkVision,” Digital Signal Processing, vol. 145, p. 104342, Feb. 2024. [Online]. Available: [link]
H. Sivakumar, V. Arvind.R, P. R. V, and G. Balamurugan, “Novel Deep Learning Pipeline for Automatic Weapon Detection,” in 2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE). Los Alamitos, CA, USA: IEEE Computer Society, Dec. 2023, pp. 1–5. [Online]. Available: [link]
M. P. Rosales, C. Rodriguez, Y. Pomachagua, and C. Navarro, “Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks,” in 2021 13th International Conference on Computational Intelligence and Communication Networks (CICN). Lima, Peru: IEEE, Sep. 2021, pp. 61–65. [Online]. Available: [link]
RHackathon, “Weapon detection dataset,” [link], aug 2023, visited on 2024-07-26. [Online]. Available: [link]
R. T. Magar and N. Shahadat, “Real-time gun detection using yolo: A deep learning approach for public safety,” in Proceedings of the 27th International Conference on Computer and Information Technology (ICCIT). Cox’s Bazar, Bangladesh: IEEE, 2024, pp. 963–967.
F. A. Warsi, M. Abdullah, M. N. Husen, M. Yahya, S. Khan, and N. Jawaid, “Gun detection system using yolov3,” in 2019 IEEE 6th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA). Kuala Lumpur, Malaysia: IEEE, 2019.
A. H. Ashraf, M. Imran, A. M. Qahtani, A. Alsufyani, O. Almutiry, A. Mahmood, M. Attique, and M. Habib, “Weapons detection for security and video surveillance using cnn and yolo-v5s,” Computers, Materials & Continua, vol. 70, no. 2, pp. 2761–2774, 2022.
S. Pawar, K. Pandit, and N. N. Chiplunkar, “Fire and gun detection using yolo v3 for surveillance system,” in International Conference on Artificial Intelligence and Data Engineering (AIDE). IEEE, 2022.
N. N C, “Smart surveillance: Deep learning-based weapon detection using yolo,” International Research Journal of Innovations in Engineering and Technology, vol. 12, pp. 2395–0056, 03 2025.
Python Software Foundation, “Python: Linguagem de programação — versão 3.10.12,” [link], 2025, linguagem de programação interpretada, utilizada neste trabalho na versão 3.10.12.
Publicado
22/10/2025
Como Citar
MOTA, Marcos Reges; CHAGAS, Ana Luísa; LIMA, Maurício Rodrigues; DIAS, Elisângela Silva.
Visão Computacional: detecção de armas utilizando YOLO V8. In: CONGRESSO LATINO-AMERICANO DE SOFTWARE LIVRE E TECNOLOGIAS ABERTAS (LATINOWARE), 22. , 2025, Foz do Iguaçu/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 26-32.
DOI: https://doi.org/10.5753/latinoware.2025.14559.
