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Evidence record 15401 · automatically gathered

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

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 synthesise

Record details

Published: 31 July 2026
Source: Artificial Intelligence Review
Category: Research
Topics: Bias & fairness · Privacy
Retrieved: 1 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Privacy

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Shared topic: Bias & fairness · Shared topic: Privacy

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arXiv cs.CY · 31 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution

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Shared topic: Bias & fairness · Shared topic: Privacy

Exploratory Integration of EEG Spectral Features and Gaze Variability for Mild Cognitive Impairment Discrimination

arXiv cs.HC · 31 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

A Computational Ethical Framework for Financial Digital Phenotyping for Mental Health

arXiv cs.CY · 28 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Ethical use of artificial intelligence in education: proposed ethical competency framework for teachers

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How to cite this record

ethics.ai (31 July 2026), “Deep learning for security-relevant event detection in visual data: a structured narrative review of the state of the art and future challenges,” evidence record 15401, https://ethics.ai/record/15401 (originally published by Artificial Intelligence Review).

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