Deep learning for security-relevant event detection in visual data: a structured narrative review of the state of the art and future challenges

Abstract

Deep learning has become a key enabling technology for detecting security-relevant events in visual surveillance data acquired from CCTV systems, UAV platforms, and other imaging sensors. However, despite substantial progress in benchmark performance, the operational deployment of such systems remains challenging due to dataset bias, domain shift, limited robustness, edge-computing constraints, and a lack of operationally meaningful evaluation metrics. This structured narrative review synthesises research published between 2021 and 2026, complemented by selected seminal studies. It is guided by predefined research questions and a documented, purposive search and selection strategy, and examines deep learning approaches for weapon detection, violence detection, anomaly recognition, person-related security tasks, perimeter monitoring, and UAV-based surveillance. The analysis covers major architectural paradigms, including YOLO-based detectors, RT-DETR, Vision Transformers, spatiotemporal networks, anomaly-detection frameworks, and edge-optimised models, with particular attention to dataset characteristics, model robustness, adversarial vulnerabilities, multimodal sensing, model compression, and regulatory considerations associated with the EU AI Act. As structuring contributions, the review proposes a multi-dimensional taxonomy that links security-event categories to architecture class, evaluation metric, sensor modality, and EU AI Act risk level, together with a critical analysis of architecture-specific failure modes under operational conditions. It further identifies recurring limitations that hinder real-world deployment: across representative studies, evaluation remains dominated by accuracy-oriented metrics such as mAP, F1-score, and FPS, whereas operational aspects including detection latency, false-alarm burden, and deployment robustness are insufficiently addressed. To bridge this gap, the review proposes Time-to-Detection (TTD) as a complementary operational evaluation framework, and highlights persistent challenges related to realistic datasets, demographic and domain biases, adversarial resilience, privacy-preserving learning, and federated deployment. The findings indicate that future research should prioritise standardised operational benchmarking, TTD-aware evaluation, robust and explainable models, realistic security datasets, efficient edge-AI deployment, and regulation-aware system design. Addressing these challenges will be essential for translating advances in deep learning into reliable and trustworthy security applications.