A Handheld Gun Detection using Faster R-CNN Deep Learning

Verma, Gyanendra K.; Dhillon, Anamika

doi:10.1145/3154979.3154988

Cited by 79 publications

(40 citation statements)

References 16 publications

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“…This can be achieved by using object detection algorithms [ 5 ] to automate the access to any premise only to people wearing masks. Object detection algorithms have been widely used in the last decade for detecting various objects like Military Gun detection [ 6 ] as well as medical purposes for cancerous cell detection [ 7 ] and other abnormalities.…”

Section: Introductionmentioning

confidence: 99%

A novel data augmentation approach for mask detection using deep transfer learning

Prusty

Tripathi

Dubey

2021

Intelligence-Based Medicine

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At the onset of 2020, the world saw the rise and spread of a global pandemic named COVID-19 which caused numerous deaths and affected millions of people around the world. Due to its highly contagious nature, this disease spread across the world within a short span of time. It forced almost all the nations to implement strict social distancing rules along with use of face masks to reduce the risk of getting infected. While the virus is still on loose, markets and business firms have reopened to keep the economy alive. This calls for modification of existing technological models to cater for the safety of individuals and stop the spread of virus in public places. One such stringent implementation to achieve this safety would be deployment of a mask detection model. The proposed mask detection models can serve as a vital utility in the coming years for ensuring proper enforcement of safety protocols. This research paper explores the use of state of the art YOLOv3 model, a deep transfer learning object detection technique, to develop a mask detection model. Along with the implementation of a standard approach of any object detection algorithm, this paper has proposed the use of a data augmentation approach for mask detection. The proposed model focuses on generating an augmented dataset from the standard dataset with the help of data augmentation done by using image filtering techniques such as grayscale and Gaussian blur. This augmented dataset is used for training the object detection model for mask detection. The mean average precision for the Data augmentation based mask detection model is observed to be 99.8% while training. Finally, a comparison on the model performance is evaluated for the standard and proposed augmented data approach. The experiment conducted showed that the average confidence level for Standard mask detection model was 0.94, 0.93, 0.91 for images of individuals (type A), images with groups of people (type B) and video with the group of people (type C) respectively. The average confidence levels for the Data augmentation based mask detection model for types A, B and C are 0.97, 0.96 0.93 respectively. This paper therefore concludes that the proposed Data augmentation based mask detection model performs better than the Standard mask detection model.

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Section: Introductionmentioning

confidence: 99%

A novel data augmentation approach for mask detection using deep transfer learning

Prusty

Tripathi

Dubey

2021

Intelligence-Based Medicine

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“…Our proposed system is further compared with the existing literature in Table 2. In [21], the proposed system includes CNN-based VGG-16 architecture as feature extractor, followed by state-of-the-art classifiers which are implemented on a standard gun database. e researchers…”

Section: Resultsmentioning

confidence: 99%

Weapon Detection Using YOLO V3 for Smart Surveillance System

Narejo

Pandey

Vargas

et al. 2021

Mathematical Problems in Engineering

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Every year, a large amount of population reconciles gun-related violence all over the world. In this work, we develop a computer-based fully automated system to identify basic armaments, particularly handguns and rifles. Recent work in the field of deep learning and transfer learning has demonstrated significant progress in the areas of object detection and recognition. We have implemented YOLO V3 “You Only Look Once” object detection model by training it on our customized dataset. The training results confirm that YOLO V3 outperforms YOLO V2 and traditional convolutional neural network (CNN). Additionally, intensive GPUs or high computation resources were not required in our approach as we used transfer learning for training our model. Applying this model in our surveillance system, we can attempt to save human life and accomplish reduction in the rate of manslaughter or mass killing. Additionally, our proposed system can also be implemented in high-end surveillance and security robots to detect a weapon or unsafe assets to avoid any kind of assault or risk to human life.

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“…The work was performed on imfdb, which in my opinion is not suitable to train a model for real-time case. They claimed to have an accuracy of 93.1% on that dataset but in the case of weapon detection, only achieving higher accuracy is not enough, and precision and recall must the considered [42]. Siham Tabik et al work was very much related to the real-time scenario.…”

Section: Related Workmentioning

confidence: 99%

Weapon Detection in Real-Time CCTV Videos Using Deep Learning

et al. 2021

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Security and safety is a big concern for today's modern world. For a country to be economically strong, it must ensure a safe and secure environment for investors and tourists. Having said that, Closed Circuit Television (CCTV) cameras are being used for surveillance and to monitor activities i.e. robberies but these cameras still require human supervision and intervention. We need a system that can automatically detect these illegal activities. Despite state-of-the-art deep learning algorithms, fast processing hardware, and advanced CCTV cameras, weapon detection in real-time is still a serious challenge. Observing angle differences, occlusions by the carrier of the firearm and persons around it further enhances the difficulty of the challenge. This work focuses on providing a secure place using CCTV footage as a source to detect harmful weapons by applying the state of the art open-source deep learning algorithms. We have implemented binary classification assuming pistol class as the reference class and relevant confusion objects inclusion concept is introduced to reduce false positives and false negatives. No standard dataset was available for real-time scenario so we made our own dataset by making weapon photos from our own camera, manually collected images from internet, extracted data from YouTube CCTV videos, through GitHub repositories, data by university of Granada and Internet Movies Firearms Database (IMFDB) imfdb.org. Two approaches are used i.e. sliding window/classification and region proposal/object detection. Some of the algorithms used are VGG16, Inception-V3, Inception-ResnetV2, SSDMobileNetV1, Faster-RCNN Inception-ResnetV2 (FRIRv2), YOLOv3, and YOLOv4. Precision and recall count the most rather than accuracy when object detection is performed so these entire algorithms were tested in terms of them. Yolov4 stands out best amongst all other algorithms and gave a F1-score of 91% along with a mean average precision of 91.73% higher than previously achieved.

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A Handheld Gun Detection using Faster R-CNN Deep Learning

Cited by 79 publications

References 16 publications

A novel data augmentation approach for mask detection using deep transfer learning

A novel data augmentation approach for mask detection using deep transfer learning

Weapon Detection Using YOLO V3 for Smart Surveillance System

Weapon Detection in Real-Time CCTV Videos Using Deep Learning

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