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Padavala, Hemaraju (2024) Performance Evaluation of Underwater Plastic Detection Model Under Diverse Environmental Conditions. Masters thesis, Dublin, National College of Ireland.
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Abstract
Protecting the environment from ocean plastic waste requires effective detection and quantification. The number rises. This is necessary for environmental protection. Neural network-based object detectors, a recent computer vision advancement, may automate aquatic plastic detection. This computer vision innovation is new. We'll examine CenterNet HourGlass104, Faster R-CNN, and YOLOv3, three popular object detection models. This study tests these models underwater. This research examines model application.
These models were tested with a large dataset that replicated underwater conditions to determine their adaptability and conservation potential. This tested whether these models could aid conservation. This list includes plastic debris with distinct traits. Different lighting and colour conditions are included.
Our findings showed neural network object detectors' marine conservation potential. Testing showed that the CenterNet HourGlass104 was the most accurate and versatile plastic contamination detector. This applied to plastic contamination detection. Faster and more accurate plastic detection may improve cleanup efforts but harm the environment and economy. This is true even in harsh environments like underwater. These implications have major effects on real-world applications.
However, these models' limitations must be acknowledged, especially in microplastic detection and high-turbidity situations. Computer vision-based systems may be optimised and used in other ways in future research. This is because these systems' environments change.
Computer vision can detect ocean plastics, but this study highlights knowledge gaps that need further research. The study proved computer vision was possible. Due to our situation, we can help fight ocean plastic pollution. Provide detailed information about CenterNet HourGlass104, Faster R-CNN, and YOLOv3's pros and cons.
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