Jul 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
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.
Abstract
Intelligent surveillance systems need very dynamic visual intelligence that will recognise individuals and mark the presence of possible threats in real-time video surveillance. To overcome this requirement, a single deep learning system called SmartVision-AI is proposed, which combines face recognition and weapon identification in one feature-based architecture. The approach uses a dual-branch convolutional encoder, attention-based feature fusion, and multi-task learning, which is optimized towards low-latency CCTV systems. Tests on mixed-face and weapon image data sets show that the architecture can deliver face recognition accuracy of 96.8%, multi-class weapon detection accuracy of 94.7%, a false-positive rate drop to 21%, 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. Further processing indicates that there is an increase in temporal stability by 32% and a reduction in the use of GPU memory by 27% in comparison with individual task-specific models. The findings attest to the fact that SmartVision-AI provides a powerful, effective, and scalable intelligent threat-aware video surveillance system.
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
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
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
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).
Chengoli prashanth, Sk.Mahammadunnisa· American Journal of AI Cyber...· 0 citations
The development of Computer vision and Deep learning frameworks plays a crucial role in Autonomous driving and real-time intelligent traffic surveillance. The Single Shot Detector (SSD) architecture is well known for its ability to perform inference quickly, and generically configured anchors are problematic because of the severe localization errors they cause. This article proposes an optimized architecture for SSD-based vehicle detection that addresses these challenges in two main steps: (1) using an optimized edge-preserving median filter to remove semantic noise in the image caused by the sensors while preserving the sharpness of the image boundaries and (2) applying custom-scaled anchor box aspect ratios specifically designed to capture the real size of the vehicles on the road. Applied to the standard KITTI Vision Benchmark dataset, the proposed framework's Mean Average Precision (mAP) and recall were 95.90% and 96.60%, respectively. The proposed model is compared with the baseline networks and is shown to significantly outperform them, achieving mAP 89.50% for vanilla SSD300 and 94.10% for YOLOv5. The proposed method reached high reliability with a balanced precision-recall rate that is highly suitable for real-time traffic monitoring systems.
Tamara A. Anai· Al-Noor Journal of Engineeri...· 0 citations
A multi-dimensional feature fusion-based face anti-spoofing detection method based on the YOLOv8 architecture that integrates dynamic optical flow features with static texture analysis and achieves robust detection through the fusion of spatial and temporal cues.
Yanhua Liang, Pengcheng Zhou, Hongmei Qin et al.· International Conference on...· 0 citations
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