Image Segmentation for Liquid Level Detection in Aircraft
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Solution Overview
Problem
Conventional methods for liquid level detection in aircraft cabins, such as verbal communications and service call buttons, result in delayed responses due to inefficiencies in request processing and crew availability, which can lead to suboptimal passenger service.
Innovation Solution
An image segmentation system that continuously captures and processes image data from cameras to detect vessels and classify liquid levels, using adaptive thresholding, erosion, dilation, and machine learning to determine the region of interest and liquid amount, thereby notifying crew members when the liquid level falls below a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional communication methods (verbal requests, service buttons) are used for liquid level detection, then passenger service is provided, but response time is delayed due to request processing inefficiencies and crew availability constraints
Solution Approach 1:
The system enables self-service by using automated image capture and processing to detect liquid levels without requiring passenger initiation or crew intervention for monitoring. The camera system continuously captures images and the processing system automatically determines when refills are needed, eliminating the need for passengers to verbally request service or press buttons.
Solution Approach 2:
The patent replaces mechanical communication systems (buttons, verbal requests) with an automated optical detection system. Instead of relying on physical interaction between passengers and crew, the system uses image capture and digital processing to monitor liquid levels and trigger notifications, substituting mechanical processes with optical and computational methods.
2Productivity
If automated image processing is implemented for liquid level detection, then response time is reduced and service efficiency is improved, but system complexity increases due to image processing requirements
Solution Approach 1:
The image processing system applies segmentation by dividing the captured image into distinct regions: the vessel, the liquid contents, and the background. This segmentation allows the system to focus processing only on relevant areas (the vessel and its contents) rather than analyzing the entire image, reducing computational complexity while maintaining detection accuracy.
Solution Approach 2:
The system extracts the region of interest (the vessel and liquid) from the full image by identifying and isolating relevant features. This extraction process removes unnecessary background and unrelated elements, simplifying the subsequent analysis and reducing the data processing burden while maintaining the ability to accurately detect liquid levels.
3Measurement precision
If continuous image capture is performed to monitor liquid levels, then detection accuracy is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic action by capturing images at scheduled intervals or based on trigger events rather than continuously monitoring at maximum capacity. The camera can capture images periodically or when specific conditions are met (such as when a vessel is detected), maintaining detection accuracy while reducing energy consumption compared to uninterrupted continuous capture at full resolution.
Data Source
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AI summary
A method for operating an image segmentation system includes receiving image data from a sensor, detecting a vessel is present in the image data, extracting a region of interest, and classifying the image data based on the region of interest.