Color-Based Foreign Object Detection Using Multi-Spectral Imaging
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Solution Overview
Problem
Current methods are inadequate for effectively detecting foreign object debris (FOD) in various industries, particularly in aviation, where FOD causes significant direct and indirect costs due to damage and malfunctions, as they fail to accurately identify deviations in color content across multiple wavelengths.
Innovation Solution
A color-based foreign object detection system captures initial images of a sample subject free of FOD, classifies colors into allowed and disallowed classes using a defined palette, and then identifies FOD in subsequent images by highlighting pixels with disallowed colors, allowing for real-time detection and user confirmation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current detection methods are used, then the system is simple to operate, but the detection precision is insufficient to accurately identify FOD
Solution Approach 1:
The color spectrum is segmented into multiple discrete color channels (red, green, blue, cyan, magenta, yellow) that are independently analyzed. This segmentation allows the system to detect FOD by comparing color content across specific wavelength ranges, improving detection precision while maintaining a manageable system structure through modular color analysis
Solution Approach 2:
The system utilizes color analysis across visible, infrared, and ultraviolet spectra to detect foreign objects. By monitoring color content changes and comparing them against baseline measurements, the system achieves high detection precision through optical property differentiation without requiring complex mechanical or electronic intervention
2Productivity
If manual inspection methods are used, then the equipment cost is low, but the productivity is reduced due to time-consuming inspection processes
Solution Approach 1:
Manual visual inspection is replaced with an automated imaging system that captures and analyzes color content across multiple spectral ranges. The system uses computer-based color classification and comparison algorithms to automatically identify FOD, dramatically increasing inspection speed while the modular architecture keeps system complexity manageable
Solution Approach 2:
The system changes the inspection parameter from simple visual observation to multi-spectral color content analysis. By measuring and comparing color values across visible, infrared, and ultraviolet ranges, the system achieves rapid automated detection that improves productivity while maintaining reasonable system complexity through focused spectral analysis
3Measurement precision
If comprehensive color analysis across multiple wavelengths is performed, then the detection precision improves, but the use of energy increases
Solution Approach 1:
The electromagnetic spectrum is segmented into three distinct analysis ranges: visible, infrared, and ultraviolet. The system activates imaging and analysis only for these specific segments rather than continuously analyzing the entire spectrum, improving detection precision through targeted spectral analysis while reducing overall energy consumption by avoiding unnecessary wavelength analysis
Solution Approach 2:
The system performs color content analysis in discrete periodic steps: capturing baseline color data, then capturing test data and comparing color content across the defined spectral ranges. This periodic rather than continuous analysis approach maintains high detection precision while significantly reducing energy consumption by activating sensors and processing only when needed
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively announces the presence of FOD before it can cause damage, enabling timely removal and reducing costs associated with delays and maintenance by accurately distinguishing between background and foreign object colors across visible, infrared, and ultraviolet spectra.
Implementation Method 1
accurately distinguishing between background and foreign object colors across visible, infrared, and ultraviolet spectra
Data Source
AI summary
Systems and methods are provided for detecting foreign objects in or on a subject of interest. A first set of images is captured of a sample subject known to be free of foreign objects. A plurality of colors from a defined color palette are classified according to a color content of the first set of images into at least first and second classes. A second set of images of the subject of interest are captured. It is determined that a foreign object is present in or on the subject of interest if a color from the first class is present in the second set of images.