Overview

MetroVelodrome, a premier outdoor cycling arena, is celebrated for its meticulously maintained, painted concrete track—an unmanned facility that is open to the public and demands rigorous safety standards. To ensure continuous operational safety and structural integrity under varying environmental conditions, MetroVelodrome partnered with us to deploy our cutting-edge XVision AI solution.

The Challenge

Regular manual inspections of the velodrome’s expansive concrete surface proved challenging due to its outdoor exposure and the dynamic usage by the public. Subtle defects such as paint peeling, cracking, and surface wear could go unnoticed, potentially compromising rider safety and leading to costly repairs. The facility required an automated, real-time monitoring system that could promptly detect and assess any surface anomalies.

The Solution

We deployed the XVision Edge system at MetroVelodrome, featuring:

• High-Performance Camera Setup:
  Two robust, IP65-rated bullet cameras with 4K resolution were strategically mounted at either end of the velodrome. Their weather-resistant design ensured reliable performance in outdoor conditions while capturing detailed imagery of the painted concrete track.

• Direct Edge Processing:
  Camera feeds were transmitted directly to the XVision Edge processing units. The system captured a snapshot of the track every 60 seconds, allowing continuous and timely monitoring of the entire surface in real time.

• Advanced Deep Learning Analysis:
  Leveraging state-of-the-art AI algorithms, the system was trained to detect a range of surface issues—from minor paint wear and fading to cracks and structural defects in the underlying concrete. The deep learning models differentiate between superficial cosmetic degradation and critical structural concerns, using a predefined risk matrix to assign a criticality score to each detected defect.

• Automated Alerts & Communication:
  When a defect is identified, the system automatically sends an email alert to the maintenance team. This alert includes a snapshot of the defect, precise location details, and an assigned criticality level based on the risk matrix. This proactive communication ensures that maintenance actions are both swift and targeted.

Results

• Enhanced Safety:
  The continuous, automated monitoring system promptly detected any degradation in the painted surface or underlying concrete, significantly reducing the risk of accidents and ensuring the safety of riders and the public.

• Operational Efficiency:
  By replacing the need for periodic manual inspections, the automated edge processing system resulted in substantial cost savings and allowed the maintenance team to focus on targeted repairs rather than broad, time-consuming surveys.

• Data-Driven Maintenance:
  Detailed analytics and historical data on surface conditions enabled MetroVelodrome to plan maintenance schedules effectively. The integration of automated alerts with criticality assessments allowed the maintenance team to prioritize repairs based on actual risk, thereby extending the facility’s lifespan and minimizing operational disruptions.

Conclusion

The deployment of the XVision AI solution at MetroVelodrome illustrates how advanced deep learning and real-time edge processing can revolutionize facility management for outdoor sports venues. By integrating high-resolution, weather-resistant cameras with direct processing on XVision Edge units, MetroVelodrome achieved a robust monitoring system that ensures the integrity and safety of its painted concrete track. The automated alert system further enhanced operational efficiency by delivering timely, actionable insights to the maintenance team.

We remain committed to partnering with forward-thinking facilities like MetroVelodrome to deliver intelligent, automated solutions that uphold the highest standards of safety, performance, and efficiency.