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Underwater Image Detection for Cleaning Purposes; Techniques Used for Detection Based on Machine Learning
Summary
Researchers reviewed machine learning techniques for underwater image detection to support water pollution cleanup, focusing on convolutional neural networks and region-based CNN methods for identifying surface mucilage and debris. The study evaluated supervised classification algorithms as the most effective approach for automated aquatic waste detection systems.
Abstract Serious problems are on the rise, especially in these current times. The world is facing too many environmental threats. Water pollution is one of the main issues threatening the future. In some parts of the world, the water’s surface is covered by mucilage, which is dangerous for both aquatic animals and humans. This article firstly defines mucilage and highlights the reasons for its production. Afterwards to tackle water pollution, cleaning systems using image detection with the help of machine learning supervised classification algorithms are highlighted. This paper showcases the machine learning and classification used as well as the best solution for convolutional neural network and region-based convolutional neural network methods.
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