Detection of underwater marine plastic debris using an augmented low sample size dataset for machine vision system: A deep transfer learning approach
Hipolito, J.C.; Sarraga Alon, A.; Amorado, R.V.; Fernando, M.G.Z.; De Chavez, P.I.C. (2021). Detection of underwater marine plastic debris using an augmented low sample size dataset for machine vision system: A deep transfer learning approach, in: 19th IEEE Student Conference on Research and Development (SCOReD 2021). pp. 82-86. https://dx.doi.org/10.1109/scored53546.2021.9652703
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Document type: Conference paper
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| Author keywords |
object detection, deep learning, transfer learning, marine plastic waste detection, yolov3 |
| Authors | | Top |
- Hipolito, J.C.
- Sarraga Alon, A.
- Amorado, R.V.
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- Fernando, M.G.Z.
- De Chavez, P.I.C.
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| 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%. |
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