Convolutional neural networks based weapon detection: a comparative study

Pradhi Anil Kumar Das; Deepak Singh Tomar

doi:10.1117/12.2622641

4 March 2022 Convolutional neural networks based weapon detection: a comparative study

Pradhi Anil Kumar Das, Deepak Singh Tomar

Proceedings Volume 12084, Fourteenth International Conference on Machine Vision (ICMV 2021); 1208418 (2022) https://doi.org/10.1117/12.2622641
Event: Fourteenth International Conference on Machine Vision (ICMV 2021), 2021, Rome, Italy

Abstract

Lately, one of the most common illegal activities include the use of shooting weapons. In such dangerous situations, there is a dire need of preventive measures that can automatically detect such munitions. This paper presents the use of computer vision and deep learning to detect weapons like guns, revolvers and pistols. Convolutional Neural Networks can be efficiently used for object detection. In this paper, precisely, two Convolutional Neural Network (CNN) architectures - Faster R-CNN with VGG16 and YOLOv3, have been used, to carry out the detection of such weapons. The pre-trained neural networks were fed with images of guns from the Internet Movie Firearms Database (IMFDB) which is a benchmark gun database. For negative case images, MS COCO dataset was used. The goal of this paper is to present and compare performance of the two models to bring about gun detection in any given scenario. The results of YOLOv3 outperforms Faster R-CNN with VGG16. The ultimate aim of this paper is to detect guns in an image accurately which in turn can aid crime investigation.

Citation Download Citation

Pradhi Anil Kumar Das and Deepak Singh Tomar "Convolutional neural networks based weapon detection: a comparative study", Proc. SPIE 12084, Fourteenth International Conference on Machine Vision (ICMV 2021), 1208418 (4 March 2022); https://doi.org/10.1117/12.2622641

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KEYWORDS

Weapons

Data modeling

Sensors

Firearms

Image processing

Databases

Video

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