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Detection of Trash in Sea Using Deep Learning
Summary
Researchers developed a deep learning convolutional neural network (CNN) model to detect and classify trash in marine and aquatic environments from underwater images, aiming to overcome the limitations of manual debris detection for objects that may be submerged or partially obscured.
Pollution in sea is one of the most agonizing issues of this generation. With that being said, water has the ability to absorb harmful chemicals that are released by debris thrown into water bodies. This can cause unimaginable effects on humans as well as sea animals. Therefore, it is vital for every one of us to take a step towards resolving this issue. Of course, there are some people who have taken a step to reduce and remove debris in sea. These measures involve manual work which may not be highly-effective as some of the debris tend to sink in water making it hard to detect. Hence, we are proposing a project that can detect and classify trash present in water bodies. It is done with the help of deep learning with CNN model. With the help of these concepts, one can determine the position and type of debris present in sea. KEYWORDS: Deep learning, trash detection, CNN model, dataset, accuracy, debris
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