Achieve up to 88% fewer false Alarms with our latest Deep Learning AID SolutionWe are pleased to present two comprehensive case studies that illustrate the significant advancements in Automatic Incident Detection (AID) systems, achieved through the integration of Citilog’s Applied Deep Learning (CT-ADL) technology.
These documents provide valuable insights into how our innovative solution is enhancing detection accuracy and operational efficiency across highways, bridges, and tunnels. 
1. Enhancing AID Efficiency on Highways and Bridges with CT-ADL Technology
This case study highlights the successful deployment of CT-ADL technology, which has led to a substantial reduction in false alarms within complex outdoor environments. By filtering out up to 88% of false alarms, the system has markedly improved operational focus and efficiency.
2. Revolutionizing Tunnel Surveillance with CT-ADL Technology
This analysis explores the transition from a legacy AID system to one powered by Deep Learning, resulting in a 75% reduction in false alarms. The report outlines how this advancement enhances safety and operational management in tunnel environments. These reports offer detailed evaluations of the technology’s impact and its potential for broader application in infrastructure security. We encourage you to explore these findings and learn how Citilog’s solutions are setting new standards in incident detection.
- World leader with 25 years of experience and 60,000 video streams analyzed in 50 countries. We offer the best performance on the market, with the highest detection rate and lowest false alarms.
- The only global company dedicated to AID, our 10 R&D engineers continually enhance our product using AI. Our unique approach blends traditional pixel-based analytics with the latest deep learning models.
- We provide the best total cost of ownership over 10 years, with standard hardware requirements, easy commissioning, annual upgrades, expert tech support, and proven cybersecurity.
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