Weapon Detection and Classification Using Deep Learning
Abstract
High gun-related crime rate poses a great threat to society in the present world. There is a serious need for systems to deal with such gun-related crimes. As CCTVs are installed in almost every part of the city, using the CCTV footage to detect the weapon is the simplest and efficient way to deal with such crimes. Unconcealed weapon detection, in images and videos, can help reduce the number of homicides due to the gun-related violence. In this work, we focus on developing a robust and automatic weapon detection system with ability to classify the detected weapon into different categories. This work provides and extensive survey on already existing weapon detection systems, weapon detection datasets, challenges in weapon detection and deep learning based object detection technologies. We have developed a new image dataset for weapon detection and classification task. The experimental analysis shows the superiority of the Faster-RCNN models over SSD models for weapon detection systems. The detection results shows how the final developed system deals with different challenges related to weapon detection and classification in real world scenarios.
Downloads
References
“Estimated number of civilian guns per capita by country,” Wikipedia, 29-Apr-2019. [Online]. Available: https://en.wikipedia.org/wiki/Estimated number of civilian guns per capita by country. [Accessed: 03-May-2019].
B. Marr, “How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read,” Forbes, 11-Mar-2019. [Online]. Available: https://www.forbes.com/sites/bernardmarr/2018/05/21/howmuch-data-do-we-create-every-day-the-mind-blowingstats-everyone-should-read/#2ecf668960ba. [Accessed: 03-May-2019].
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
G. K. Verma and A. Dhillon, “A Handheld Gun Detection using Faster R-CNN Deep Learning,” Proceedings of the 7th International Conference on Computer and Communication Technology - ICCCT-2017, 2017.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, 2017.
Simonyan K., Zisserman A., “Very deep convolutional networks for large scale image recognition,” ICLR, 2015.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
R. Olmos, S. Tabik, and F. Herrera, “Automatic handgun detection alarm in videos using deep learning,” Neurocomputing, vol. 275, pp. 66–72, 2018.
S. Akcay, M. E. Kundegorski, C. G. Willcocks, and T. P. Breckon, “Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 9, pp. 2203–2215, 2018.
H. Nakahara, H. Yonekawa and S. Sato, "An object detector based on multiscale sliding window search using a fully pipelined binarized CNN on an FPGA," 2017 International Conference on Field Programmable Technology (ICFPT), Melbourne, VIC, 2017, pp. 168-175.
R. Girshick, “Fast R-CNN,” 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
Dai, J.; Li, Y.; He, K. & Sun, J. Lee, D. D.; Sugiyama, M.; Luxburg, U. V.; Guyon, I. & Garnett, R. (Eds.), “R-FCN: Object Detection via Region-based Fully Convolutional Networks Advances”, Neural Information Processing Systems (NIPS) 29, 2016, 379-387.
J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
J. Lai, “Developing a Real-Time Gun Detection Classifier,” 2017.
Sermanet, Pierre, David Eigen, Xiang Zhang, MichaA˜ l Mathieu, Rob Fergus and Yann LeCun. “OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks,” CoRR, 2014, abs/1312.6229.
Tzutalin “LabelImg”. Git code (2015). https://github.com/tzutalin/labelImg
Copyright (c) 2024 ITEGAM-JETIA
This work is licensed under a Creative Commons Attribution 4.0 International License.
