{
  "id": 15401,
  "url": "https://link.springer.com/article/10.1007/s10462-026-11653-z",
  "title": "Deep learning for security-relevant event detection in visual data: a structured narrative review of the state of the art and future challenges",
  "summary": "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",
  "authors": null,
  "category": "research",
  "topics": "bias-fairness,privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-31T00:00:00.000Z",
  "fetched_at": "2026-08-01T05:10:57.676Z",
  "source_slug": "x-artificial-intelligence-review",
  "source_name": "Artificial Intelligence Review",
  "source_homepage": "https://link.springer.com/journal/10462",
  "ethics_ai_record_url": "https://ethics.ai/record/15401",
  "original_url": "https://link.springer.com/article/10.1007/s10462-026-11653-z",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}