Deep Learning-Based Predictive Analytics for Weapon Detection: A Fusion of FMR-CNN and YOLOv9 Models
DOI:
https://doi.org/10.21015/vtse.v13i2.2136Abstract
The early detection of firearms from surveillance videos is necessary to improve public security and life. Even with the growing advancement of Deep Learning (DL) techniques, difficulties persist in distinguishing generic items from weapons in surveillance footage. While deep learning has significantly advanced generic object identification, weapons detection demands specialized methods that maintain high accuracy and processing efficiency. This study addresses these challenges by presenting a novel hybrid deep learning system that combines the strengths of Faster R-CNN, Mask R-CNN, and the YOLOv9s architecture. The aim is to enhance surveillance systems through predictive analytics and automatic weapon identification for defensive security measures. This research focuses on deep learning to create an intelligent detection system that improves operational effectiveness and recognition accuracy. The testing results indicate that the hybrid technique outperforms the individual models, achieving an overall accuracy of 99.02%, along with comparable precision and recall scores. In contrast, YOLOv9s alone achieved an accuracy of 98.70%, while other standalone models showed lower performance. However, the technique still faces limitations in poorly lit or visually cluttered environments.The proposed framework represents a flexible, scalable, and precise tool for instantaneous weapon spotting, contributing substantially to intelligent monitoring technologies and community protection efforts.
References
United Nations Office on Drugs and Crime. [Online]. Available: https://unodc.exposure.co/
W. H. T. Spaight, “Operational requirements for security measures,” in Proc. IEEE 34th Annu. Int. Carnahan Conf. Security Technol., Ottawa, ON, Canada, 2000, pp. 64–70, doi: 10.1109/CCST.2000.891169.
C. Deng, M. Wang, L. Liu, Y. Liu, and Y. Jiang, “Extended Feature Pyramid Network for Small Object Detection,” IEEE Trans. Multimedia, vol. 24, pp. 1968–1979, 2022, doi: 10.1109/TMM.2021.3074273.
K. A. Hashmi, A. Pagani, M. Liwicki, D. Stricker, and M. Z. Afzal, “CasTabDetectoRS: Cascade Network for Table Detection in Document Images with Recursive Feature Pyramid and Switchable Atrous Convolution,” J. Imaging, vol. 7, no. 10, p. 214, Oct. 2021, doi: 10.3390/jimaging7100214.
F. Hajri and H. Fradi, “Vision Transformers for Road Accident Detection from Dashboard Cameras,” in Proc. 18th IEEE Int. Conf. Adv. Video Signal Based Surveillance (AVSS), Madrid, Spain, 2022, pp. 1–8, doi: 10.1109/AVSS56176.2022.9959545.
A. Burgos-Madrigal, V. Romero-Bautista, R. Díaz-Hernández, and L. Altamirano-Robles, “Evaluation of Deformable Convolution: An Investigation in Image and Video Classification,” Mathematics, vol. 12, no. 16, p. 2448, Aug. 2024, doi: 10.3390/math12162448.
C.-S. Sung and J. Y. Park, “Design of an intelligent video surveillance system for crime prevention: applying deep learning technology,” Multimed. Tools Appl., vol. 80, no. 26–27, pp. 34297–34309, Nov. 2021, doi: 10.1007/s11042-021-10809-z.
P. Yadav, N. Gupta, and P. K. Sharma, “A comprehensive study towards high-level approaches for weapon detection using classical machine learning and deep learning methods,” Expert Syst. Appl., vol. 212, p. 118698, Feb. 2023, doi: 10.1016/j.eswa.2022.118698.
S. Natha et al., “Deep BiLSTM Attention Model for Spatial and Temporal Anomaly Detection in Video Surveillance,” Sensors, vol. 25, no. 1, p. 251, Jan. 2025, doi: 10.3390/s25010251.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” arXiv preprint, Jun. 2016. [Online]. Available: https://arxiv.org/abs/1506.02640
W. Liu et al., “SSD: Single Shot MultiBox Detector,” in Proc. ECCV, Cham, Switzerland: Springer, 2016, pp. 21–37, doi: 10.1007/978-3-319-46448-0_2.
R. Olmos, S. Tabik, and F. Herrera, “Automatic handgun detection alarm in videos using deep learning,” Neurocomputing, vol. 275, pp. 66–72, Jan. 2018, doi: 10.1016/j.neucom.2017.05.012.
G. K. Verma and A. Dhillon, “A Handheld Gun Detection using Faster R-CNN Deep Learning,” in Proc. 7th Int. Conf. Comput. Commun. Technol., Allahabad, India, 2017, pp. 84–88, doi: 10.1145/3154979.3154988.
S. N. Bushra et al., “Smart Video Surveillance Based Weapon Identification Using Yolov5,” in Proc. 2022 Int. Conf. Electron. Syst. Intell. Comput., Chennai, India, 2022, pp. 351–357, doi: 10.1109/ICESIC53714.2022.9783499.
S. Singh et al., “Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment,” Multimed. Tools Appl., vol. 80, no. 13, pp. 19753–19768, May 2021, doi: 10.1007/s11042-021-10711-8.
A. Kiran, P. Purushotham, and D. D. Priya, “Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications,” in Proc. 2022 Int. Conf. Adv. Smart Secure Intell. Comput., Bhubaneswar, India, 2022, pp. 1–5, doi: 10.1109/ASSIC55218.2022.10088403.
S. Khalid et al., “Weapon detection system for surveillance and security,” in Proc. 2023 Int. Conf. IT Innov. Knowl. Discovery (ITIKD), Manama, Bahrain, 2023, pp. 1–7, doi: 10.1109/ITIKD56332.2023.10099733.
W. E. I. B. W. N. Afandi and N. M. Isa, “Object Detection: Harmful Weapons Detection using YOLOv4,” in Proc. 2021 IEEE Symp. Wireless Technol. Appl. (ISWTA), Shah Alam, Malaysia, 2021, pp. 63–70, doi: 10.1109/ISWTA52208.2021.9587423.
U. G. et al., “Crime Scene Object Detection from Surveillance Video by using Tiny YOLO Algorithm,” in Proc. 2023 Int. Conf. Pervasive Comput. Social Netw. (ICPCSN), Salem, India, 2023, pp. 654–659, doi: 10.1109/ICPCSN58827.2023.00114.
L. Sumi and S. Dey, “A Deep Learning Approach to Gun Detection in Surveillance System using YOLOv9,” in Proc. 2024 Control Instrum. Syst. Conf. (CISCON), Manipal, India, 2024, pp. 1–5, doi: 10.1109/CISCON62171.2024.10696152.
T. Santos, H. Oliveira, and A. Cunha, “Systematic review on weapon detection in surveillance footage through deep learning,” Comput. Sci. Rev., vol. 51, p. 100612, Feb. 2024, doi: 10.1016/j.cosrev.2023.100612.
P. More, S. Patil, and T. Pattanshetti, “Real time Violence and Weapon detection and Alert System,” preprint, May 21, 2024, doi: 10.21203/rs.3.rs-4389485/v1.
D. Berardini et al., “A deep-learning framework running on edge devices for handgun and knife detection from indoor video-surveillance cameras,” Multimed. Tools Appl., Jul. 2023, doi: 10.1007/s11042-023-16231-x.
Md. M. Mukto et al., “Design of a real-time crime monitoring system using deep learning techniques,” Intell. Syst. Appl., vol. 21, p. 200311, Mar. 2024, doi: 10.1016/j.iswa.2023.200311.
P. Yadav, N. Gupta, and P. K. Sharma, “WeaponVision AI: a software for strengthening surveillance through deep learning in real-time automated weapon detection,” Int. J. Inf. Technol., vol. 17, no. 3, pp. 1717–1727, Apr. 2025, doi: 10.1007/s41870-024-02375-y.
M. T. Bhatti et al., “Weapon Detection in Real-Time CCTV Videos Using Deep Learning,” IEEE Access, vol. 9, pp. 34366–34382, 2021, doi: 10.1109/ACCESS.2021.3059170.
S. Ahmed et al., “Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos,” Appl. Sci., vol. 12, no. 12, p. 5772, Jun. 2022, doi: 10.3390/app12125772.
M. M. Fernandez-Carrobles, O. Deniz, and F. Maroto, “Gun and Knife Detection Based on Faster R-CNN for Video Surveillance,” in Pattern Recognit. Image Anal., Cham, Switzerland: Springer, 2019, vol. 11868, pp. 441–452, doi: 10.1007/978-3-030-31321-0_38.
C.-Y. Wang, I.-H. Yeh, and H.-Y. Mark Liao, “YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information,” in Proc. ECCV, Cham, Switzerland: Springer, 2025, vol. 15089, pp. 1–21, doi: 10.1007/978-3-031-72751-1_1.
Z. Ge et al., “YOLOX: Exceeding YOLO Series in 2021,” arXiv preprint, 2021, doi: 10.48550/arXiv.2107.08430.
L. Pang, H. Liu, Y. Chen, and J. Miao, “Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm,” Sensors, vol. 20, no. 6, p. 1678, Mar. 2020, doi: 10.3390/s20061678.
I. Vijoli and C.-C. Vancea, “Towards Improving Real-Time Surveillance Systems with Enhancement Techniques for Deep Learning Models,” in Proc. 2024 IEEE Int. Conf. Intell. Comput. Commun. Process. (ICCP), Cluj-Napoca, Romania, Oct. 2024, pp. 1–8, doi: 10.1109/ICCP63557.2024.10793047.
R. Mahaveerakannan, B. R., and P. Subramanian, “Intelligent Video Surveillance for Weapon Detection: A Novel Approach for Low-Cost, Effective Monitoring in Low-Light Environments,” in Proc. 2024 Int. Conf. Sustain. Commun. Netw. Appl. (ICSCNA), Theni, India, 2024, pp. 1669–1676, doi: 10.1109/ICSCNA63714.2024.10863851.
S. Yellapragada et al., “CCTV-Gun: Benchmarking Handgun Detection in CCTV Images,” arXiv preprint, 2023, arXiv:2303.10703.
L. Sumi and S. Dey, “YOLOv5-based weapon detection systems with data augmentation,” Int. J. Comput. Appl., vol. 45, no. 4, pp. 288–296, Apr. 2023, doi: 10.1080/1206212X.2023.2182966.
P. Aggarwal et al., “Advanced Computational Approaches to Gun Detection with CNN-SVM Model,” in Proc. 2024 Int. Conf. Emerg. Technol. (INCET), Belgaum, India, May 2024, pp. 1–6, doi: 10.1109/INCET61516.2024.10592905.
N. Hnoohom et al., “Weapon Detection Using Faster R-CNN Inception-V2 for a CCTV Surveillance System,” in Proc. 2021 Int. Conf. Sci. Electron. Commun. (ICSEC), Rai, Thailand, Nov. 2021, pp. 400–405, doi: 10.1109/ICSEC53205.2021.9684649.
A. Goenka and K. Sitara, “Weapon Detection from Surveillance Images using Deep Learning,” in Proc. 2022 Int. Conf. Emerg. Technol. (INCET), Belgaum, India, May 2022, pp. 1–6, doi: 10.1109/INCET54157.2022.00001.
Dhiraj and D. K. Jain, “An evaluation of deep learning-based object detection strategies for threat object detection in baggage security imagery,” Pattern Recognit. Lett., vol. 120, pp. 112–119, Apr. 2019, doi: 10.1016/j.patrec.2019.01.014.
A. V. Ramana et al., “Abnormal Behavior Prediction in Elderly Persons Using Deep Learning,” in Proc. 2023 Int. Conf. Comput., Electron. Electr. Eng. Appl. (IC2E3), Srinagar Garhwal, India, Jun. 2023, pp. 1–5, doi: 10.1109/IC2E357697.2023.10262547.
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