Open Access
Issue |
E3S Web Conf.
Volume 391, 2023
4th International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2023)
|
|
---|---|---|
Article Number | 01071 | |
Number of page(s) | 18 | |
DOI | https://doi.org/10.1051/e3sconf/202339101071 | |
Published online | 05 June 2023 |
- Ahmed, S., Bhatti, M.T., Khan, M.G., Lövström, B., & Shahid, M., Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos, Proceedings of Applied Sciences (Switzerland), 12(12). (2022). [Google Scholar]
- Ashraf, A.H., Imran, M., Qahtani, A.M., Alsufyani, A., Almutiry, O., Mahmood, A., Attique, M., & Habib, M. Weapons detection for security and video surveillance using CNN and YOLO-V5s. Proceedings of Computers, Materials and Continua, 70(2), 2761–2775. (2022). [CrossRef] [Google Scholar]
- Baig, M.S., & Khan, P.A., Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications. Proceedings of International Journal of Advanced Research in Science and Technology, 12(10), 127–135. (2020) [Google Scholar]
- Bhatti, M.T., Khan, M.G., Aslam, M., & Fiaz, M.J., Weapon Detection in Real-Time CCTV Videos Using Deep Learning. Proceedings of IEEE Access, 9, 34366–34382. (2021). [CrossRef] [Google Scholar]
- Fathy, C., & Saleh, S.N., Integrating Deep Learning-Based IoT and Fog Computing with Software-Defined Networking for Detecting Weapons in Video Surveillance Systems. Sensors, 22(14). (2022). [Google Scholar]
- Galab, M.K., Taha, A., & Zayed, H.H., Adaptive Technique for Brightness Enhancement of Automated Knife Detection in Surveillance Video with Deep Learning, Arabian Journal for Science and Engineering, 46(4), 4049–4058. (2021) [CrossRef] [Google Scholar]
- Gawade, S., Vidhya, R., & Radhika, R., Automatic Weapon Detection for Surveillance Applications. Proceedings of the International Conference on Innovative Computing and Communication, 1–6. (2022) [Google Scholar]
- Hashmi, T.S.S., Haq, N.U., Fraz, M.M., & Shahzad, M. Application of Deep Learning for Weapons Detection in Surveillance Videos. Proceedings of International Conference on Digital Futures and Transformative Technologies, October. (2021) [Google Scholar]
- Jain, H., Vikram, A., Mohana, Kashyap, & Jain, A. Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications, Proceedings of the International Conference on Electronics and Sustainable Communication Systems, 193–198. (2020) [Google Scholar]
- Salido, J., Lomas, V., Ruiz-Santaquiteria, J., & Deniz, O. Automatic handgun detection with deep learning in video surveillance images. Applied Sciences, 11(13), 1–17 (2021) [Google Scholar]
- Hnoohom, N., Chotivatunyu, P., Maitrichit, N., Sornlertlamvanich, V., Mekruksavanich, S., & Jitpattanakul, A. Weapon Detection Using Faster R-CNN Inception-V2 for a CCTV Surveillance System. Proceedings of the 25th International Computer Science and Engineering Conference, 400–405. (2021). [Google Scholar]
- Ekmal, M., Quyyum, E., Haris, M., & Abdullah, L. Proceedings of the Multimedia University Engineering Conferenced. Atlantis Press International BV. (2023). [Google Scholar]
- Kaya, V., Tuncer, S., & Baran, A. Detection and classification of different weapon types using deep learning. Applied Sciences, 11(16), 1–13. (2021) [Google Scholar]
- Xu, S., & Hung, K., Development of an AI-based System for Automatic Detection and Recognition of Weapons in Surveillance Videos, Proceedings of the IEEE 10th Symposium on Computer Applications and Industrial Electronics, 48–52. (2020). [Google Scholar]
- T Hamsini, Lokhande, H.V, Nithisiri, S., & L, R., A Review on Weapon Detection and Alert System Using Deep Neural Networks, Proceedings of International Research Journal of Modernization in Engineering Today and Science, 4(06), 410–413. (2022). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.