Liquid Petroleum Hydrocarbon Ocean Coastal Water Pollution Identification Using Deep Neural Network

All marine life is put at peril when oil spills damage the marine ecosystem. Sea otters, and birds lose their natural protection against the cold and wet because oil breaks down their natural insulation and leads to hypothermia, therefore oil must be cleaned. Some of the effective ways of cleaning oils can include using chemical solvents, skimmers and dispersants and other methods. But all these methods have its own disadvantages when oils get dispersed from one place to another place in the ocean. Many methods like random-walk method, noise removal, and dark spot detection are used. Compared to these methods Convolutional Neural Network (CNN) has high computing and processing ability, the CNN architecture that may be used to semantically split SAR pictures into several categories. The suggested CNN is optimized for use as there is no physical contact required and also not implementing the sensors in ocean that could be costly. The advantages of the present Convolutional Neural Network (CNN) are CNNs have been shown to achieve high accuracy in a variety of computer vision tasks, including image classification, object detection, and segmentation. It has the Ability to learn complex features, Robustness to noise and Real-time performance while other methods fail to do. The oil is detected the alert message is sent as an SMS to the administrator.