An AI-powered automatic incident detection solution for tunnels
Tunnelwatch is an Automatic Incident Detection (AID) software which uses Deep Learning algorithms to improve the precision of real-time reported information in tunnels.
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A tunnel is a particularly confined and therefore critical environment, always exposed to traffic challenges.
Because of their constraints and responsabilities, tunnel operators are always looking for improved efficiency. Yet, the limitations of current AID solutions are glaring: tedious installation, lack of reliability, heavy maintenance, equipment dependence, etc.
Thanks to the latest AI technologies, Tunnelwatch provides a robust real-time incident detection solution for Supervisory Tunnel Center: it improves decision support while facilitating maintenance, for even more safety in tunnels.
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Precision and reliability
Robust and intelligent detection, for more accurate and reliable data: use of Artificial Intelligence to eliminate false alarms.
Facilitated set-up and maintenance
Adaptive solution, with automated configuration: intelligent mask generation model for automated lane segmentation.
An open and autonomous system
Compatible with most cameras (no specific equipment constraints), making easier system maintainability and evolution.
Shared intelligence
Cross-tunnel learning: pooling of false alarm cases for shared intelligence that benefits all structures.
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CONTROL INTERFACE
A simple and ergonomic interface dedicated to an efficient supervision for operators (integrator or supervisor)
AUTOMATED MASKING
Automated masking: during installation (automatic generation), as well as over time (adaptive maintenance)
CONTEXT ANALYSIS
Considering context (space, time, current incidents) to refine detections and consolidate correlated alarms
INTER-CAMERA INTELLIGENCE
Identification of the same vehicle from one camera to the next, enabling a double check of alarms and camera prioritization
> 98 %
incidents detected
1 year
of remote cloud-based tests
> 20 million
vehicles analysed
TUNNELWATCH IN TOULON
PROJECT MANAGER
Toulon Tunnel Control and Safety Station
BEFORE TUNNELWATCH
33% false alarms 20% incorrect incident classification
AREA COVERED
Installation in Toulon of a Tunnelwatch analyser for the 94 DAI camera video streams and a back-office server in the technical room of CET ESCOTA
A successful evaluation
Cyclope.ai and ESCOTA called on renowned partners to properly evaluate the performance of the Tunnelwatch solution deployed in Toulon.
Lombardi SA was mandated to define a testing and performance analysis methodology and to carry out the execution of the project (night tests).
Our solution relies on AI models, pre-trained in underground structures and with several thousands of incident sequences
Thanks to Cyclope’s expertise in image processing, Deep Learning, and its ultra-powerful AI models for object recognition, Tunnelwatch eliminates almost all false alarms.
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Tunnelwatch continously improves through successive re-training phases that integrate all the situations encountered in the tunnels in which the solution is deployed. The exposure in all tunnels in which Tunnelwatch operates is thus shared to improve the overall performance of the system.
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Designed to meet the needs of tunnel supervisors, Tunnelwatch is the result of a collaboration between technology experts and tunnel operators. It generates qualified data, capable of providing relevant information aiding the operator through his decision-making process, thus ensuring an even safer tunnel for users.
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Tunnelwatch was designed to facilitate the work of integrators, by making interactions with the global supervision system and maintenance easier: API easy to integrate, self-configuring solution thanks to an automatic masking system, which automatically readapts in the event of a change.
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OUTDOOR AID
Detecting incidents throughout the highway network thanks to outdoor cameras: towards 100% virtualised security patrols
AUTONOMOUS VEHICLES
Providing an I2V data stream (infrastructure-to-vehicle) to help drive autonomous vehicles
© Cyclope.ai 2020
