Deep learning-based oil spill detection with LWIR camera
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초록
The ocean covers approximately 71% of the total surface area of Earth and plays a significant role in maintaining the envi-ronment and ecosystems. Oil spills are the largest source of pollution in the ocean, mainly Bunker C oil and diesel oil used as vessel fuels. Therefore, oil spill detection is essential for marine protection, which motivated this study. Detection with radar, which is based on electromagnetic waves, is achieved using satellite synthetic aperture radar (SAR); thus, real-time detection over a small range is difficult. Hence, in this study, an oil spill detection system based on thermal imaging using a long-wave infrared (LWIR) camera is proposed. The oil spill detection algorithm utilizes the You Only Look Once (YOLO) model, which is widely used for object detection. In addition, 1,644 thermal images were labeled to evaluate the proposed system. The training and test results showed an accuracy of 96.91% and false alarm rate of 8.33%. An improved detection performance can be expected from subsequent experiments using larger image datasets.
Abstract
The ocean covers approximately 71% of the total surface area of Earth and plays a significant role in maintaining the envi-ronment and ecosystems. Oil spills are the largest source of pollution in the ocean, mainly Bunker C oil and diesel oil used as vessel fuels. Therefore, oil spill detection is essential for marine protection, which motivated this study. Detection with radar, which is based on electromagnetic waves, is achieved using satellite synthetic aperture radar (SAR); thus, real-time detection over a small range is difficult. Hence, in this study, an oil spill detection system based on thermal imaging using a long-wave infrared (LWIR) camera is proposed. The oil spill detection algorithm utilizes the You Only Look Once (YOLO) model, which is widely used for object detection. In addition, 1,644 thermal images were labeled to evaluate the proposed system. The training and test results showed an accuracy of 96.91% and false alarm rate of 8.33%. An improved detection performance can be expected from subsequent experiments using larger image datasets.
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