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.
Chengoli prashanth, Sk.Mahammadunnisa· American Journal of AI Cyber...· 0 citations
Recent advancements in deep learning have enabled the generation of realistic images directly from natural language descriptions. This paper presents a semantic-aware framework for text-to-face image synthesis using a joint Bidirectional Long Short-Term Memory (BiLSTM) network and Generative Adversarial Network (GAN). The proposed approach simultaneously trains the text encoder and image generator, allowing effective learning of semantic relationships between textual attributes and facial features. Initially, input descriptions are transformed into meaningful vector representations using Bi-LSTM, which are then utilized by the GAN to synthesize high-quality facial images. Unlike conventional methods that rely on separately trained text encoders, the proposed end-to-end architecture improves semantic consistency and visual realism. The model is trained on the CelebA dataset with corresponding facial descriptions and evaluated using similarity and image quality measures. Experimental results demonstrate improved face generation accuracy and better preservation of facial attributes, making the framework suitable for applications in forensic investigations, digital character creation, intelligent human-computer interaction, and public safety systems.
Heena Anjum, Sk.Mahammadunnisa· American Journal of AI Cyber...· 0 citations