Computer Vision Hazard Detection in Facilities
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Solution Overview
Problem
Conventional methods for evaluating hazardous conditions in facilities, such as slippery floors and obstructed pathways, are complex, expensive, and prone to human error, leading to infrequent safety checks and increased slip-and-fall injuries.
Innovation Solution
A computer-implemented system using image processing and machine learning to analyze digital images from cameras, detect hazards, and send alerts when a predetermined threshold is exceeded, enabling continuous monitoring and improved safety management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional techniques are used to evaluate hazardous conditions, then safety checks can be performed, but the complexity and cost increase and human error occurs leading to infrequent checks
Solution Approach 1:
The patent replaces manual visual inspection with an automated computer vision system using imaging devices and image processing algorithms. The system automatically detects hazardous conditions such as wet floors and obstructed pathways, eliminating human error and reducing the complexity of safety monitoring while improving reliability through consistent automated assessment.
Solution Approach 2:
The system enables self-monitoring of safety conditions through automated image capture and analysis. The imaging devices and processing system operate autonomously to detect and report hazardous conditions without requiring human intervention, allowing facilities to continuously monitor safety independently.
2Measurement precision
If conventional safety checks are performed frequently, then hazard detection improves, but the cost and effort increase reducing check frequency
Solution Approach 1:
The patent implements continuous automated monitoring through imaging devices that continuously capture images of facility areas. The system processes images in real-time to detect hazardous conditions, enabling uninterrupted safety surveillance without requiring periodic manual inspections, thus improving detection accuracy while eliminating time loss associated with frequent human checks.
3Reliability
If manual evaluation of hazardous conditions is performed, then safety assessment can be made, but human error increases and alerts are missed
Solution Approach 1:
The patent replaces human evaluation with automated image processing and machine learning algorithms that objectively analyze images for hazardous conditions. The system consistently applies detection criteria without fatigue or distraction, eliminating human error while improving reliability through automated, standardized assessment of safety conditions.
Data Source
AI summary
Systems and methods for detecting a hazard in a facility include the use of one or more cameras coupled with a hazard detection server. The hazard detection server is adapted to analyze images from the cameras, determine probabilities of hazards being present in the images, and provide an alert to a manager or workers when the probabilities exceed a hazard threshold.