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Surendran, S.
- A Deep Learning Based Analysis of Oil Spilled Images To Minimize Pollution in Marine Environment
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Authors
Affiliations
1 Department of Computer Science and Engineering, Veltech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College, India., IN
2 Department of Computer Science and Engineering, Tagore Engineering College, India., IN
3 Department of Marine Engineering, AMET Deemed to be University, India., IN
1 Department of Computer Science and Engineering, Veltech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College, India., IN
2 Department of Computer Science and Engineering, Tagore Engineering College, India., IN
3 Department of Marine Engineering, AMET Deemed to be University, India., IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 3 (2023), Pagination: 2914-2920Abstract
The rising demand for oil and increased shipping capacity are significant contributors to the pollution of the world seas and oceans that is caused by human activity. Oil spills on the world waterways are another major cause of this pollution. Because of the growing demand for oil and the capability of the maritime transport industry, oil spills on seas and oceans have become a significant source of pollution in recent years. It is of the utmost importance that oil spills are constantly monitored and that measures are taken to clean them up as quickly as is humanly possible. This is since oil spills can have devastating effects not only on the local ecosystem but also on the economies of states that are located along the shore. Because of the ongoing threats that are posed to marine life, biodiversity, and habitats, it is of the utmost importance to be able to keep a watch on oil spills from a distance, recognise them, and take action to clean them up. This is essential. In the past ten years, developments in remote sensing data collection, computing capability, cloud computing infrastructure, and cuttingedge SqueezeNet algorithms have led to significant advancements in oil spill detection. These developments have been responsible for most of the progress. These technological advancements have made it possible to identify oil spills more accurately.Keywords
Oil Spill, Shipping, Pollution, SqueezeNet.
References
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Abstract Views :124 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, Veltech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, IN
2 Department of Computer Science and Engineering, Tagore Engineering College, Chennai, IN
3 Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, IN
4 Department of Artificial Intelligence and Data Science, Karpagam Institute of Technology, IN
1 Department of Computer Science and Engineering, Veltech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, IN
2 Department of Computer Science and Engineering, Tagore Engineering College, Chennai, IN
3 Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, IN
4 Department of Artificial Intelligence and Data Science, Karpagam Institute of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 4 (2023), Pagination: 2953-2958Abstract
The decline of marine ecosystems poses a substantial threat to the viability of local economies that are reliant on marine life for their continued survival. Artificial intelligence (AI) and machine learning (ML) are two of the several developing technologies that have the ability to address environmental challenges. In particular, ML may be used to better analyse the oceans, keep track of shipping, maintain track of debris in the ocean, unregulated and unreported (IUU) fishing, ocean mining, reduce coral bleaching, and stop the spread of marine diseases. In this paper, we examine the rising prospects and concerns related with the application of AI in the maritime environment, as well as their potential scalability for larger results, using some use-cases to illustrate our points. The results that were obtained when the model prediction was applied to random images are evidence that the model that was suggested provides better outcomes with fewer data points.Keywords
SAR, Ocean, Pollution, Deep Intelligence, Detection.References
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