Conserving Oceans from Plastic Waste using Convolution Neural Network Model

Plastic Pollution is posing a serious threat to the ocean and its diversity to an extreme level. The number of plastic bags, bottle and other plastic materials which are disposed into the ocean not only pollute its water, tend to change its properties but also can kill marine animals and cause the marine diversity to degrade. Internet of Things (IoT) is a new technology which has sensing capabilities and work in a real-time environment. Aquatic IoT sensors can be used in order to detect and tract these plastic objects in the water. By this method, the cleaning of oceans can be done in an effective manner. There are various deep learning models that have the potential to classify the objects in a real-time environment either on land or in water. These models can be used in order to detect plastic waste from marine debris. In this paper a two-step approach has been adopted with the aim to detect and classify plastic objects under ocean water. Firstly, the IoT sensors are deployed deep into the ocean which take pictures of the different regions in the ocean. Next, the VGG16 CNN (Convolutional Neural Network) deep learning model has been adopted for this paper which takes these pictures as input and classify them into plastic, plants, animals and other objects so that amount of plastic waste can be detected. The approach showed 96.5% accuracy as compared to other object detection and classification approaches. More effectiveness in the proposed work can be added by ensembling the random boost classifier and decision tree approach in the future.