Slippery Surface Detection Using Multi-Camera Segmentation
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Solution Overview
Problem
Current systems lack an effective automated method to detect slippery surfaces in facilities, particularly in low-light conditions and with glare or discoloration, which hinders the identification of hazardous surface conditions, leading to increased risks of slips and falls.
Innovation Solution
A system utilizing multiple cameras positioned at different angles, integrated light sources, and machine learning algorithms to classify floor areas as slippery or non-slippery, with user feedback for training, and motion detection sensors to identify slippery conditions and related events, ensuring accurate detection and alerting users in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms are used to detect slippery surfaces, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the detection task by using multiple cameras positioned at different angles to capture various aspects of the floor surface. Each camera provides a specific viewpoint that contributes to the overall detection accuracy, allowing the machine learning algorithm to process divided visual information rather than relying on a single complex sensor
Solution Approach 2:
The machine learning algorithm serves multiple functions: it classifies floor areas as slippery or non-slippery, processes combined visual inputs from multiple cameras, and adapts through user feedback. This multi-functionality consolidates what would otherwise require separate systems into a single intelligent processing unit, improving accuracy without proportionally increasing complexity
2Reliability
If multiple cameras are used to capture floor areas from different angles, then detection reliability is improved, but device complexity increases
Solution Approach 1:
The floor area is segmented into multiple viewing zones, each captured by a specifically positioned camera. This segmentation allows the system to cover the entire floor area comprehensively while maintaining manageable complexity for each individual camera unit
Solution Approach 2:
The system merges the visual inputs from multiple cameras into a combined visual input that is processed by a single machine learning algorithm. This combining approach consolidates the data from multiple sources into a unified detection process, improving reliability without requiring separate processing systems for each camera
3Measurement precision
If automated lighting is integrated with cameras, then detection capability in low-light conditions is improved, but energy consumption increases
Solution Approach 1:
The automated lighting system operates periodically or on-demand rather than continuously, activating only when cameras need to capture images in low-light conditions. This periodic operation provides sufficient illumination for detection while minimizing overall energy consumption compared to continuous lighting
Solution Approach 2:
The automated lighting acts as an intermediary that enables camera operation in low-light environments without requiring the cameras to operate continuously. The lighting provides conditional support only when environmental light is insufficient, improving detection capability while avoiding constant energy consumption
4Measurement precision
If user feedback is collected and used for training, then algorithm accuracy is improved, but processing time increases
Solution Approach 1:
The system implements a feedback mechanism where user confirmations of slippery conditions are collected and used to iteratively train the machine learning algorithm. This feedback loop progressively improves algorithm accuracy by incorporating real-world validation data, transforming initial detection errors into learning opportunities rather than permanent failures
Solution Approach 2:
The system performs preliminary detection and classification before final confirmation, allowing it to present only uncertain or potential cases to users for verification. This preliminary processing reduces the overall processing time by pre-filtering cases that require user feedback, rather than requiring user input for every detection scenario
Data Source
AI summary
According to some embodiments, disclosed are systems and methods for machine learning-based image detection and the determination of slippery conditions based therefrom. The disclosed systems and method identify a set of images that depict captured imagery in relation to at least one area of a floor at a location. These images are then analyzed via at least one slippery condition detection machine learning algorithm, which results in a determination of a classification of the area of the floor (e.g., does a puddle exist or other type of slippery condition). This information is stored and later used for training of the at least one slippery condition detection machine learning algorithm. Moreover, the information is communicated to beacons in/around the location, to alert users to the condition.