Detection of Underwater Marine Plastic Debris Using an Augmented Low Sample Size Dataset for Machine Vision System: A Deep Transfer Learning Approach
Japhet C. Hipolito, Alvin Sarraga Alon, Ryndel V. Amorado, Maricel Grace Z. Fernando, Poul Isaac C. De Chavez
Abstract
Waste in aquatic environments devastates aquatic habitats and offers a tall environmental and economical risk. Machine Vision might play a role in resolving this issue by detecting and finally eliminating debris. Using an augmented low sample size from a publicly available collection of underwater plastic waste, this research employed a YOLOv3 deep-learning system to visually recognize debris in realistic underwater environments. The detection model has a training and validation accuracy of 98.026 % and 94.582 %, respectively, according to the study's findings, with an mAP value of 98.15%. With its effectiveness in detecting underwater plastic waste, the recommended model is suitable for a variety of machine vision systems. The system has a 100% testing accuracy, with detection per frame accuracy ranging from 60.59% to 98.89%.