A major aviation refuelling farm sought to transform its operational environment through the deployment of advanced video analytics. The initiative focused on automating critical tasks—from real-time vehicle verification and PPE compliance to hazard detection and perimeter security—to minimize human error, ensure regulatory compliance, and provide actionable, data-driven insights.
The facility, a key player in aviation fuel logistics, manages high volumes of vehicles and personnel within a sensitive and high-risk operational environment. With critical areas including pump houses, calibration stations, unloading and loading bays, and expansive perimeters, the need for robust surveillance and efficient process automation was paramount.
Operating under stringent safety and operational protocols, the facility required a solution that could seamlessly integrate multiple surveillance functions. This included managing vehicle entry/exit cycles, monitoring personnel safety through PPE compliance, differentiating between genuine security threats and harmless wildlife, and promptly detecting hazards in high-risk zones such as pump houses and tank farms.
Manual monitoring and verification at the refuelling farm led to operational inefficiencies and heightened risks. Traditional surveillance struggled to keep pace with the complex demands of the facility, where delayed detection of unauthorized access, safety non-compliance, or operational hazards could result in significant safety breaches and costly disruptions.
Vehicle Management: High workload and manual verification difficulties in managing vehicle entry and exit cycles. Safety Compliance: Inconsistent enforcement of PPE usage increases accident risks for personnel. Perimeter Security: Difficulty distinguishing between urban wildlife and unauthorized human activity, leading to false alarms. Operational Hazards: Limited ability to detect early signs of fires, leaks, or spills in pump houses and tank farms. Equipment Calibration: Ensuring accurate and safe operations at test rigs and calibration stations. Inspection Inefficiencies: Slow, manual reporting and issue resolution hamper timely corrective actions.An AI-powered video analytics system was deployed to address these challenges across multiple use cases by integrating advanced computer vision and deep learning techniques:
Vehicle Entry and Exit:
An Automatic Number Plate Recognition (ANPR) system captures and processes license plate data in real time. This enables accurate vehicle verification, reduces manual errors, and provides cycle time information for each vehicle. Personnel Safety Compliance Monitoring:
High-definition cameras equipped with face recognition technology continuously monitor PPE compliance among personnel. Automated alerts are triggered when non-compliance is detected, ensuring a safer work environment. Perimeter Intrusion & Wildlife Detection:
Deep learning algorithms are employed to distinguish between human intrusions and harmless wildlife. This minimizes false alarms while ensuring that any genuine security breaches prompt immediate action. Unloading & Loading Bay Safety Monitoring:
Video analytics continuously scan these high-risk zones to verify proper equipment usage and adherence to safety protocols, enabling quick corrective actions when unsafe practices are detected. Pump House & Tank Farm Incident Detection:
Advanced image processing techniques monitor critical areas for visual cues of hazards—such as flames, smoke, or leaks—facilitating early emergency alerts and reducing potential downtime. Test Rig & Calibration Monitoring:
The system oversees test rig operations to ensure compliance with standard operating procedures. It detects anomalies in equipment handling and calibration processes to prevent operational failures. Integrated Mobile Inspection & Reporting:
A mobile inspection platform enables field staff to capture images, annotate incidents, and submit real-time reports, thereby accelerating decision-making and corrective actions.
A comprehensive requirement gathering process was conducted in close collaboration with facility stakeholders. This phase involved identifying critical monitoring areas, defining use case requirements, and formulating a detailed, phased implementation plan.
Hardware Installation: Ruggedized, weather-resistant cameras and networking devices were installed strategically throughout the facility to ensure comprehensive coverage, even in challenging environmental conditions. Software Integration: The AI models—ranging from ANPR to deep learning-based object recognition—were seamlessly integrated with the existing IT infrastructure. Data communication protocols were established to ensure real-time processing and alerting across all modules. Training and Optimization:
Post-deployment, the system underwent iterative model tuning based on initial feedback. Customized training sessions were conducted for on-site personnel, ensuring they could effectively operate the system and interpret real-time analytics. Continuous Monitoring and Support:
An agile project management approach facilitated regular performance reviews and on-site trials. Continuous monitoring, coupled with proactive stakeholder communication, ensured that the system maintained optimal performance, with risk mitigation measures in place to address any operational challenges swiftly. Specialized Hardware Installation:
Deploy ruggedized, weather-resistant, and flame-proof cameras along with advanced networking equipment throughout the aviation fuel farm. These devices are strategically installed in high-risk areas such as pump houses, calibration stations, and unloading/loading bays to ensure continuous, reliable performance under extreme safety conditions. Iterative AI Model Deployment:
Roll out customized AI models in phases to address critical functions like hazard detection, vehicle tracking with ANPR, and PPE compliance monitoring. This iterative approach allows for continuous refinement and optimization based on real-world feedback from high-hazard environments. System Integration & Dashboard Development:
Seamlessly integrate multiple surveillance and analytics modules into a unified, user-friendly dashboard. This centralized platform is designed for remote operations, offering real-time insights and alerts even in low-bandwidth conditions, crucial for rapid decision-making in a high-risk setting. Comprehensive Testing & Validation:
Conduct rigorous, on-site testing under various operational conditions to validate system performance and safety compliance. Each milestone is marked by successful pilot runs and performance benchmarks, ensuring that the system meets the stringent safety and hazard detection standards required for aviation fuel operations. Enhanced Vehicle Processing: Automated ANPR reduced manual verification time and improved vehicle cycle time tracking. Improved Safety Compliance: Real-time PPE monitoring led to higher adherence rates, reducing safety incidents. Accurate Hazard Detection: Early identification of potential hazards in pump houses and tank farms minimized downtime and prevented accidents. Increased Operational Confidence: Automated alerts and centralized data provided management with actionable insights, ensuring rapid response to potential threats. Streamlined Processes: Integration of mobile inspection and real-time reporting tools reduced manual oversight, accelerating corrective actions and improving overall efficiency. Robust Security: Advanced object recognition and perimeter monitoring systems enhanced the facility’s ability to secure sensitive areas, contributing to a safer operational environment.
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