Aquacleaner – An AI Enabled Robotic Trash Boat to Collect Floating Trash

Abstract

Effective trash management and environmental sustainability are crucial challenges that demand accurate classification of waste materials. Traditional sorting methods often fall short in achieving the precision necessary for optimizing recycling processes. This article presents an innovative approach utilizing Convolutional Neural Networks (CNNs) to automatically classify waste materials, including plastics, paper, glass, organic debris, cardboard, and metals. CNNs are a great fit for this purpose because of their ability to extract complex patterns from input images and learn them. Using a number of experiments containing fine-tuning approaches and hyperparameter modifications, the proposed system is optimized after being trained on a broad dataset of trash images. This training process enhances the ability of the model to accurately differentiate between various waste types. When tested on a real-world garbage dataset, the CNN-based approach demonstrated superior classification accuracy and robustness, outperforming conventional machine learning techniques. The results highlight the potential of CNNs in improving waste sorting systems, leading to more efficient recycling processes. By accurately identifying and categorizing waste materials, this method supports environmental sustainability efforts by enhancing sorting and recycling efficiency. The proposed approach offers a scalable solution, making it a valuable tool for addressing waste management challenges and contributing to broader environmental sustainability goals.