IoT-Based Ocean Cleanup: Detecting and Classifying Marine Litter with Convolutional Neural Networks

Ocean ecosystems and animals are in danger from the globalization of marine litter. In the context of this environmental catastrophe, it provides a Convolutional Neural Network (CNN)-based solution to Internet of Things (IoT)-based marine litter identification and categorization. It uses underwater cameras and sensors to capture a comprehensive database of marine waste floating in the ocean. Our suggested CNN architecture targets real-time identification and categorization of common marine trash such as plastic bottles, bags, and fishing nets. It uses transfer learning and substantial data augmentation approaches to train and fine-tune the model, resulting in impressive detection and classification accuracy. To deploy our system in harsh and far-flung maritime conditions, use a scalable and energy-efficient IoT infrastructure. Extensive testing in the field proves the viability and efficacy of the IoT-based strategy for tracking and reducing marine waste. To help politicians and environmentalists make informed choices about ocean cleaning initiatives, the technology delivers useful data in real-time via detection and classification. Leveraging the potential of IoT and deep learning technology can help in the continuing efforts to protect and preserve the seas.