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4 January 2024 Oil Spill Detection and Classification from Airborne EOIR Images Using a Deep Learning Model
Ngoc An Bui, Young Gon Oh, Im Pyeong Lee
Author Affiliations +
Abstract

Bui, N.A.; Oh, Y.G., and Lee, I.P., 2023. Oil spill detection and classification from airborne EOIR images using a deep learning model. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 279-283. Charlotte (North Carolina), ISSN 0749-0208.

The marine ecological environment is adversely affected by oil spills, which necessitates effective and rapid treatment options. While current research mainly focuses on detecting oil spills, detection alone is insufficient. It is necessary to provide information regarding the types of oil involved in the accident, the mass of each oil type, and other relevant data. In this research, a dataset consisting of patrol videos captured by EOIR cameras mounted on Korean Coast Guard helicopters, along with internet-collected data, was utilized to train a DaNet deep learning model for the purpose of oil spill detection and classification. The results indicate that the DaNet model can detect oil with a mean accuracy of 83.48% and a mean Intersection over Union (mIoU) of 72.54%. Moreover, the model can classify four types of oil with a macro-average F1-score of 83.91%. This study also demonstrates that using the DaNet decoder results in 6.14% higher accuracy than PsPnet.

Ngoc An Bui, Young Gon Oh, and Im Pyeong Lee "Oil Spill Detection and Classification from Airborne EOIR Images Using a Deep Learning Model," Journal of Coastal Research 116(sp1), 279-283, (4 January 2024). https://doi.org/10.2112/JCR-SI116-057.1
Received: 6 March 2023; Accepted: 21 April 2023; Published: 4 January 2024
KEYWORDS
dual attention mechanism
EOIR imagery
instance segmentation
Oil spill
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