Marine Debris Recognition with Artificial Intelligence (Detecção de resíduos marinhos utilizando inteligência artificial)

Resumo

https://doi.org/10.5281/zenodo.20601206

Abstract

Marine pollution, particularly solid waste and plastics, poses an escalating threat to marine ecosystems, biodiversity, and human health. Contrary to the outdated perception of oceans as limitless waste repositories, it is now understood that plastic materials persist for centuries, fragmenting into microplastics that infiltrate the food chain. Research indicates that approximately 11 million metric tons of plastic enter the oceans annually, contributing to an accumulated total of nearly 200 million tons. In Brazil, this issue is exacerbated by intense port activities and insufficient waste management infrastructure. This reality underscores the urgent need for effective monitoring and prevention strategies. This study proposes a machine learning-based solution for the automatic detection and classification of marine debris. We evaluated multiple architectures, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), to determine the most effective approach for identifying pollution in underwater imagery. The results demonstrate that the CNN model achieved the highest performance, with 82.5% accuracy. Furthermore, we developed a web-based platform to democratize access to these environmental insights, aiming to assist NGOs and government agencies in mitigating critical pollution hotspots.