Armament Detection Using Deep Learning

Abstract:

In the recent past there has been an increase in the occurrence of violent incidents involving dangerous objects such as arms and knives. Being able to quickly identify and defuse such situations are of utmost importance in order to preserve peace and to avoid human casualties. One of the most important and commonly used methods to increase security is the usage of surveillance cameras almost everywhere. The benefit of object detection techniques can be used in this field in order to help improve security. Using object detection techniques in order to detect objects of interest in surveillance footage is one method to identify dangerous situations and take necessary steps in order to minimise any damages.This paper uses convolutional neural network (CNN) based YOLO algorithm in its implementation to detect weapons such as knives and pistols

• Periodical: Advances in Science and Technology (Volume 124)
• Pages: 228-235
• DOI: https://doi.org/10.4028/p-3g2p6g
• Online since: February 2023
• Authors: Rohan George Jose*, Rithvik Rajmohan
• Keywords: Artificial Intelligence (AI), Computer Vision, Weapon Detection, YOLO-V4

References

[1] H. Jain, A. Vikram, Mohana, A. Kashyap and A. Jain, Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications,, 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 2020, pp.193-198,.

DOI: 10.1109/icesc48915.2020.9155832

[2] M. T. Bhatti, M. G. Khan, M. Aslam and M. J. Fiaz, Weapon Detection in Real-Time CCTV Videos Using Deep Learning,, in IEEE Access, vol. 9, pp.34366-34382, 2021,.

DOI: 10.1109/access.2021.3059170

[3] Handgun Detection Using Combined Human Pose and Weapon Appearance Jesus Ruiz-Santaquiteria; Alberto Velasco-Mata;Noelia Vallez;Gloria Bueno;Juan A. Álvarez-García;Oscar Deniz IEEE Access.

DOI: 10.1109/access.2021.3110335

[4] Mery, Domingo; Svec, Erick; Arias, Marco; Riffo, Vladimir; Saavedra, Jose M.; Banerjee, Sandipan (2017). Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(4), 682–692.

DOI: 10.1109/tsmc.2016.2628381

[5] A. Egiazarov, V. Mavroeidis, F. M. Zennaro and K. Vishi, Firearm Detection and Segmentation Using an Ensemble of Semantic Neural Networks,, 2019 European Intelligence and Security Informatics Conference (EISIC), 2019, pp.70-77,.

DOI: 10.1109/eisic49498.2019.9108871

[6] Wei Liu et al., SSD: Single Shot MultiBox Detector,, European Conference on Conputer Vision, Volume 169, pp.20-31 Sep. (2017).

[7] D. Erhan et al., Scalable Object Detection Using Deep Neural Networks,, IEEE Conference on Computer Vision and Pattern Recognition(CVPR),(2014).

DOI: 10.1109/cvpr.2014.276

[8] Ruben J Franklin et.al., Anomaly Detection in Videos for Video Surveillance Applications Using Neural Networks,, International Conference on Inventive Systems and Control,(2020).

DOI: 10.1109/icisc47916.2020.9171212

[9] H R Rohit et.al., A Review of Artificial Intelligence Methods for Data Science and Data Analytics: Applications and Research Challenges,,2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), (2018).

DOI: 10.1109/i-smac.2018.8653670

[10] Abhiraj Biswas et. al., Classification of Objects in Video Records using Neural Network Framework,, International conference on Smart Systems and Inventive Technology,(2018).

DOI: 10.1109/icssit.2018.8748560

[11] Pallavi Raj et. al.,Simulation and Performance Analysis of Feature Extraction and Matching Algorithms for Image Processing Applications, IEEE International Conference on Intelligent Sustainable Systems,(2019).

DOI: 10.1109/iss1.2019.8907990

[12] Mohana et.al., Simulation of Object Detection Algorithms for Video Survillance Applications,, International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), (2018).

DOI: 10.1109/i-smac.2018.8653665

[13] Yojan Chitkara et. al.,Background Modelling techniques for foreground detection and Tracking using Gaussian Mixture model, International Conference on Computing Methodologies and Communication,(2019).

DOI: 10.1109/iccmc.2019.8819825

[14] Rubner et.al, A metric for distributions with applications to image databases,, International Conference on Computer Vision,(2016).

[15] N. Jain et.al., Performance Analysis of Object Detection and Tracking Algorithms for Traffic Surveillance Applications using Neural Networks,, 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), (2019).

DOI: 10.1109/i-smac47947.2019.9032502

[16] A. Glowacz et.al., Visual Detection of Knives in Security Applications using Active Appearance Model,,Multimedia Tools Applications, (2015).

DOI: 10.1007/s11042-013-1537-2

[17] S. Pankanti et.al.,Robust abandoned object detection using regionlevel analysis,,International Conference on Image Processing,(2011).

[18] Ayush Jain et.al.,Survey on Edge Computing - Key Technology in Retail Industry, International Conference on Intelligent Computing and Control Systems,(2019).

[19] Mohana et.al., Performance Evaluation of Background Modeling Methods for Object Detection and Tracking," International Conference on Inventive Systems and Control,(2020).

DOI: 10.1109/icisc47916.2020.9171192

[20] J. Wang et.al., Detecting static objects in busy scenes,, Technical Report TR99-1730, Department of Computer Science, Cornell University, (2014).

[21] V. P. Korakoppa et.al., An area efficient FPGA implementation of moving object detection and face detection using adaptive threshold method,, International Conference on Recent Trends in Electronics, Information & Communication Technology,(2017).

DOI: 10.1109/rteict.2017.8256870

[22] S. K. Mankani et.al., Real-time implementation of object detection and tracking on DSP for video surveillance applications,,International Conference on Recent Trends in Electronics, Information & Communication Technology,(2016).

DOI: 10.1109/rteict.2016.7808180