[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121716-en":3,"doc-seo-121716-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121716,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","An Efficient approach for Firearms Detection using Machine Learning - Abstract & Results","The study addresses the global impact of gun-related violence by proposing a computer-based system for automatic firearms detection, with a focus on pistols. Leveraging recent machine learning progress in recognition and object detection, the system uses the You Only Look Once (YOLO v3) model trained on a personalized dataset. Results indicate YOLO v3 surpasses traditional CNN models and YOLO v2, while training does not require high computation resources or intensive GPUs.","|  |  |\n| --- | --- |\n|  | \u003Cbr>VFAST Transactions on Software Engineering [http://vfast.org/journals/index.php/VTSE@ 2023](http://vfast.org/journals/index.php/VTSE@ 2023), ISSN(e): 2309-3978, ISSN(p): 2411-6246\u003Cbr>Volume 11, Number 2, April-June 2023 pp:94-99 |\n\nAn Efficient approach for Firearms Detection using Machine Learning  \nAamna Rahoo 1, Fizza Abbas Alvi 1, Ubaidullah Rajput 1, Imtiaz Ali Halepoto2  \n1Department of Computer Systems Engineering Quaid-e Awam University of Engineering Science Teachnology Nawabshah, Pakistan  \n2 Department of Software Engineering Quaid-e Awam University of Engineering Science Teachnology Nawabshah, Pakistan  \n*Corresponding author email: [fizza_alvi@quest.edu.pk](fizza_alvi@quest.edu.pk)  \nABSTRACT  \nEach year, there is a significant number ofpeople impacted by gun-related violence globally. To address this issue, we have created a computer-based system that can automatically identify firearms, specifically pistol. Recent advancements in machine learning has shown success in the fields of recognition and object detection. Our system utilizes the You Only Look Once (YOLO V3) object detection model, which was trained on a personalized dataset. Our training results indicate that YOLO V3 outperforms both traditional convolutional neural network (CNN) models and YOLO V2. Notably, our approach did not require high computation resources or intensive GPUs to train our model. By incorporating this YOLO V3 model into our security system, we hope to rescue lives and decrease the occurrence of manslaughter or mass killings. Moreover, detecting weapons or other dangerous materials and preventing harm or risk to human life could be accomplished by integrating this system into sophisticated surveillance and security robots.  \nKEYWORDS  \nCNN, YOLO V3, GPUs, Security robots  \nJOURNAL INFO  \nHISTORY: Received:April 17, 2023 Accepted: June 20, 2023 Published: June 27, 2023  \nINTRODUCTION  \nEach year, many people lose their lives due to incidents involving guns. Children living in areas with high levels of violence or exposed to it through the media are particularly vulnerable to psychological trauma. Any anyone who has been a part of gun-related violence, whether as a victim, a perpetrator, or a bystander, may endure both immediate and long-term psychological problems. Studies indicate that handheld pistols are the main weapons used indifferent crimes, such as robberies, loot, and assault. To reduce such offences, early identification of disruptive behavior and careful monitoring of suspicious activities are necessary so that law enforcement authorities can respond right away. [1]  \nGun-related violence extent differ significantly between geographical regions and nations. It is estimated that the worldwide fatality rate resulting from the use of firearms could reach up to 1,000 deaths per day. [2]  \nBased on statistical data, the annual death toll from mass shootings in Pakistan is 4.2 per 100,000 people. These shootings, ranging from street crimes to attacks on individual institutions, have resulted in the loss of many precious lives. This highlights the fact that the currently using a manual security system still requires human inspection to spot suspicious activity, which takes a lot of time to report such incidents to security officials, who then have to respond to the circumstances.  \nWhile the natural human visual system is fast, accurate, and capable of performing complex tasks such as identifying different objects and recognizing obstacles with  \nminimal conscious effort, it is a well-known fact that if an individual observes the same thing for an extended period, there is a possibility of fatigue and inattentiveness.  \nToday, we can efficiently train computers and create automated computer-based systems because large datasets, faster GPUs, sophisticated machine learning algorithms, and improved algorithms are readily available that can accurately detect and identify multiple items on a website.  \nRe","cbCaivdL82ff2jSP","https://ap.wps.com/l/cbCaivdL82ff2jSP","pdf",616596,1,6,"English","en",105,"# Abstract\n## Introduction\n## Related Work\n## System Objective","[{\"question\":\"What detection system does the document propose for firearms identification?\",\"answer\":\"It proposes a computer-based system using the You Only Look Once (YOLO v3) object detection model trained on a personalized dataset to detect firearms, specifically pistols.\"},{\"question\":\"How does the proposed YOLO v3 approach compare with YOLO v2 and traditional CNN models?\",\"answer\":\"The training results indicate that YOLO v3 outperforms both traditional CNN models and YOLO v2.\"},{\"question\":\"What advantage does the document claim regarding training resources?\",\"answer\":\"The approach is reported to not require high computation resources or intensive GPUs to train the model.\"}]","An Efficient approach for Firearms Detection using Machine Learning - 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