An efficient hybrid CNN-transformer framework for real-time weapon detection and face recognition

The growing demand of smart surveillance systems necessitates the accurate and real-time detection of weapons and face recognition with robustness against occlusion, illumination changes, and complex backgrounds. Existing techniques based on standalone CNN or transformer architectures are less effective in capturing local fine-grained features as well as long-range dependencies. This paper presents ConViDeTR, a hybrid deep learning framework that integrates CNN, Vision Transformer (ViT), and Detection Transformer (DETR) architectures into a unified framework. The key contribution of the proposed framework is the deep feature fusion layer, which integrates local spatial features, global context features, and object query features in one shared feature space. This enables the synchronous execution of weapon detection and face recognition using one efficient framework. The experiments using existing benchmark datasets validate the performance, achieving 98.9% accuracy in weapon detection and 97.34% accuracy in face recognition and outperforming the existing techniques in both tasks. The framework also demonstrates real-time performance with 25-30 FPS and low latency. The performance of the proposed framework sustains its effectiveness, robustness, and scalability in the development of next-generation intelligent surveillance systems.