Visual-Based Detection Method for Oil Leakage in Antarctic Power-Generation Cabin
Authors: 
Xiao Ge, Tao Wang, Kanjian ZhangAuthors Info & Claims
Xiao Ge, Tao Wang, Kanjian ZhangAuthors Info & ClaimsPages 271 - 279
Abstract
In the unattended periods of Antarctic Power-Generation Cabin, it is imperative to promptly identify oil leakage within the cabin to assess potential malfunctions in the oil storage module or generator. We proposed a visual-based method for oil leakage detection in the Antarctic Power-Generation Cabin, employing an orbital-type inspection robot platform. The detection method integrates an improved MBLLEN image enhancement algorithm, utilizing color features in the HSV color space for image segmentation and combines prior knowledge with perspective transformation for size estimation. The proposed image enhancement algorithm outperforms the mainstream algorithms in terms of brightness enhancement and color restoration. We also established an image set depicting oil leakage scenarios within the cabin where the method achieved an accuracy of about 98% and a precision of about 10 <Formula format="inline"><TexMath><?TeX $c{m}^2$?></TexMath><File name="a00--inline1" type="gif"/></Formula>. Furthermore, it exhibited resilience to robot positioning errors and insensitivity to varying lighting conditions, thereby demonstrating robust applicability.
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Index Terms
- Visual-Based Detection Method for Oil Leakage in Antarctic Power-Generation Cabin
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Published In
February 2024
757 pages
ISBN:9798400709234
DOI:10.1145/3651671
Copyright © 2024 ACM.
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Published: 07 June 2024
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- the National Natural Science Foundation of China
- Shenzhen Science and Technology Program
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ICMLC 2024
ICMLC 2024: 2024 16th International Conference on Machine Learning and Computing
February 2 - 5, 2024
Shenzhen, China
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