Automated Hazard Detection for Fueling Forecourts
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Solution Overview
Problem
The current fueling forecourt environment faces hazardous scenarios such as fires, fuel leaks, and unsafe vehicle operations, which are often detected too late due to reliance on human monitoring, leading to potential injuries and property damage.
Innovation Solution
A system utilizing cameras and predictive models for automated hazard detection, which compares video feed data to model objects to identify hazards and triggers alerts or mitigation actions, such as activating fire suppression systems or deactivating fueling station components, to address these risks in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human monitoring is used to detect hazards, then the system is simpler and easier to operate, but the detection time is delayed and response is slower
Solution Approach 1:
The patent replaces the mechanical human monitoring system with an automated computer vision system using cameras and machine learning models. The system captures video feeds from multiple cameras, processes them through predictive models to detect hazards like fire, fuel leaks, and unsafe vehicle operations, and triggers automated alerts. This substitution eliminates human reaction time delays while maintaining operational simplicity through automated processing.
2Loss of time
If automated hazard detection systems are implemented, then the detection speed and response time improve, but the device complexity increases
Solution Approach 1:
The patent divides the hazard detection system into multiple independent components: separate cameras for different viewing angles, individual predictive models for specific hazard types (fire detection model, fuel leak detection model, unsafe operation model), and modular alert mechanisms. Each component operates independently but contributes to the overall detection capability, making the complex system manageable and maintainable through functional segmentation.
3Measurement precision
If multiple predictive models are used for comprehensive hazard detection, then the measurement precision and hazard identification accuracy improve, but the computational requirements and system complexity increase
Solution Approach 1:
The patent employs a universal video feed processing architecture that serves multiple hazard detection purposes. The same video input stream is processed by different predictive models simultaneously (fire detection, fuel leak detection, unsafe operation detection), allowing one system to perform multiple detection functions. This multi-functionality approach achieves comprehensive hazard identification without requiring separate dedicated systems for each hazard type, thereby managing complexity while maintaining high detection accuracy.
Data Source
AI summary
In one aspect, data characterizing a video feed acquired by a camera oriented toward and including a field of view of a forecourt of a fueling station can be received. The video feed can be monitored for hazards, and the monitoring of the video feed can include performing automatic hazard detection on the video feed using at least one predictive model that predicts a presence of a hazard within the forecourt of the fueling station. A command can be transmitted in response to the detecting of the presence of the hazard within the forecourt of the fueling station. Related apparatus, systems, methods, techniques, and articles are also described.