Abstract
Marine transportation has high economic importance because the majority of the physical goods trades occurs via sea routes. However, minor and major oil spills can occur for a variety of natural and man-made reasons, resulting in partial or complete leaks from ships. Because of the limited opportunities for rescue in the mid-ocean, this situation has the potential to be fatal. Therefore real-time monitoring of the oil spills from ships is a huge challenge. Due to the availability of advanced satellites equipped with Synthetic Aperture Radar (SAR), it is now possible to monitor oil spills in oceans in real-time. Earlier, deep learning was widely used for this purpose; however, it requires a large amount of training data as well as computational power. We propose a novel image processing technique based on blob detection that considerably reduces computational resource usage while maintaining consistent detection accuracy. We achieved detection accuracy of up to 95.3% and detected an oil spill patch within 6.9 milliseconds. The proposed model outperforms deep learning methods by 20 times faster detection with a tradeoff of only 0.8% accuracy. The proposed modelling approach will enable automated real-time detection ship oil spills at low cost in terms of infrastructure and maintenance.
Original language | English |
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Title of host publication | 2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665497817 |
DOIs | |
State | Published - 2022 |
Publication series
Name | 2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022 |
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Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Blob Detection
- Computer Vision
- Deep Learning
- Image Processing
- Marine Oil Spill
- Synthetic Aperture Radar
ASJC Scopus subject areas
- Control and Optimization
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Signal Processing
- Electrical and Electronic Engineering