Jul 2026· American Journal of AI Cyber Computing Management· 0 citations· 1 references
TL;DR
DeepGuard is presented, an intelligent deep learning framework for automated weapon detection in images and surveillance videos using Faster Region-Based Convolutional Neural Network (Faster R-CNN) and Single Shot Detector (SSD).
Abstract
Weapon-related threats in public places demand intelligent surveillance systems capable of detecting dangerous objects accurately and in real time. This paper presents DeepGuard, an intelligent deep learning framework for automated weapon detection in images and surveillance videos using Faster Region-Based Convolutional Neural Network (Faster R-CNN) and Single Shot Detector (SSD). The proposed framework employs annotated weapon datasets for model training and utilizes convolutional neural networks to identify and localize weapons with bounding boxes. A comparative evaluation of SSD and Faster RCNN is conducted to analyze their detection accuracy and inference speed. Experimental results demonstrate that Faster R-CNN achieves superior detection accuracy, whereas SSD provides faster processing suitable for real-time applications. The developed system effectively identifies weapons in diverse surveillance environments, enhancing public safety through early threat detection and continuous monitoring. The proposed framework offers a reliable, scalable, and intelligent solution for smart surveillance systems, making it suitable for deployment in airports, railway stations, educational institutions, commercial buildings, and other high-security environments.
This study proposes an Advanced Surveillance Framework that makes use of YOLOv10, a next-generation real-time object detection algorithm that greatly outperforms conventional single-sensor approaches in precision, recall, and real-time responsiveness.
Sadiya Begum, Lubna Nausheen, Ruqiya Fatima· International Journal of Eng...· 0 citations
The results demonstrate that a modular, open-source, multi-model architecture can provide broad surveillance coverage, cloud-based auditability, and flexibility for adding new detection capabilities while maintaining practical real-time performance.
Hanan Syed Shabir, Noor Fatima, Safia Baloch et al.· 0 citations
Tests show that the proposed SmartVision-AI architecture can deliver face recognition accuracy, multi-class weapon detection accuracy, and a precision increase of up to 19%, and a processing time of only 38 ms/frame, which can be effectively deployed in near real-time.
P.Shobana, V. S. Raja, P. S. Rajakumar et al.· International journal of com...· 0 citations
This project proposes an AI-powered threat detection system capable of automatically detecting weapons in real time from CCTV footage, specifically focusing on pistols. Security in modern society is a growing concern, especially for countries aiming to create a safe environment for investors and tourists. While Closed Circuit Television (CCTV) cameras are widely used for surveillance, they still depend on human oversight. This project addresses the need for an automated system capable of detecting illegal activities, specifically weapons, in real-time using CCTV footage. Current deep learning techniques, despite advancements in hardware and software, face challenges such as occlusions, viewing angles, and varied environments. This project proposes a solution utilizing state-of-the-art deep learning algorithms for weapon detection, focusing on pistol detection using a custom dataset created from various sources, including manual collections, YouTube, GitHub, and public databases. Through testing multiple models, YOLOv4 emerged as the most effective with an F1-score of 91% and mean average precision (mAP) of 91.73%.
Keywords--Weapon Detection, Deep Learning, Object Detection, Artificial Intelligence, Computer Vision, Real-time Surveillance
Dr.Pothuraju V V Satyanarayana, Chittiboina Syamala· International Scientific Jou...· 0 citations
Road infrastructure plays a vital role in ensuring safe and efficient transportation, making timely detection of road damage essential for reducing accidents and maintenance costs. Traditional inspection methods are often slow, expensive, and dependent on manual observation, making them unsuitable for largescale monitoring. This project presents an automated road damage detection system that uses Unmanned Aerial Vehicle (UAV) images and deep learning techniques to identify defects such as cracks and potholes. High-resolution aerial images are captured using UAVs and processed through advanced YOLO-based object detection models to accurately locate and classify damaged road sections. The collected images are preprocessed to improve detection performance, and the trained model is evaluated using standard performance metrics. The proposed system provides faster inspection, minimizes human effort, and enables continuous monitoring of road conditions over large areas. The results demonstrate that UAV-assisted deep learning offers a reliable and efficient solution for modern road infrastructure maintenance and supports timely repair planning.
Aelaboina sai sruthi, Dr.G.Purna, Chandar Rao· American Journal of AI Cyber...· 0 citations
The high rate of growth in road traffic has been a major cause of drastic increase in the number of accidents which have resulted in high demand of effective real-time monitoring and response systems. This paper introduces a hybrid system of accident detection and classification of severity based on CCTV video streams implemented on deep learning. The suggested system is a combination of the YOLOv8s object detector model and a Convolutional Neural Network (CNN) using MobileNetV3 to guarantee the efficient performance of the system. YOLOv8s is used to identify regions of accidents in the continuous video frames and the CNN model is used to categorize the identified accidents into moderate and severe. The system is trained on a publicly available dataset of more than 15,000 annotated images, which were recorded in a variety of environmental conditions, which guarantees a robust system and generalization. Experimental findings indicate that it has a high detection accuracy with good precision, recall and mAP values and stable classification accuracy. Also, an automated alert system is included to inform emergency services in case of severe accidents identified so that they can respond quicker and have a lesser effect. The suggested framework is minimal and scalable and appropriate to be implemented in intelligent transportation systems and smart city applications.
J. Viswanath, Jyothinagaram Harika, Gajulapalli Karishma et al.· 2026 4th International Confe...· 0 citations
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