Charge-Coupled Frequency Response Multispectral Inversion Network-Based Detection Method of Oil Contamination on...

Area of Science:

• Aviation Safety
• Image Processing
• Environmental Monitoring

Background:

• Aircraft operations can lead to runway oil spills from fuel, lubricants, or maintenance errors.
• Conventional RGB imaging struggles to differentiate oil spills from similar-looking contaminants like sewage.
• Accurate identification of oil spills is critical for flight safety and accident investigations.

Purpose of the Study:

• To develop an effective method for detecting oil spills on airport runways.
• To enhance the accuracy of oil spill detection by leveraging spectral information.
• To improve upon existing RGB-based detection limitations using advanced image processing.

Main Methods:

• Developed a spectral reconstruction technique to enhance RGB images into Hyperspectral Imaging (HSI) images.
• Utilized the MST++ spectral reconstruction network model for image enhancement.
• Employed the Fast R-CNN model for oil spill detection on the generated HSI images.

Main Results:

• The spectral reconstruction method significantly improved oil spill detection accuracy and completeness by 25.3% and 26.5% respectively, compared to RGB images.
• The Fast R-CNN model achieved a 5% increase in Intersection over Union (IOU) when applied to HSI images.
• The MST++ model demonstrated superior performance in reconstructing RGB images into multispectral images for enhanced oil detection.

Conclusions:

• Spectral reconstruction-based HSI images offer superior precision and accuracy for oil spill detection on runways compared to traditional RGB images.
• This technique effectively utilizes inherent spectral information of oil spills, boosting detection efficacy.
• The proposed method presents a novel, efficient approach for improving aviation safety and environmental protection through enhanced oil spill detection.