A Deep Learning Based Analysis of Oil Spilled Images To Minimize Pollution in Marine Environment

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.