Mobile geo-tagging and cloud-based underwater garbage identification using Convolutional Neural Network
Balbin, J.R.; Sejera, M.M.; Al-Sagheer, Z.N.; Castañeda, J.A.N.B.; Francisco, V.A.V. (2021). Mobile geo-tagging and cloud-based underwater garbage identification using Convolutional Neural Network, in: Jiang, X. et al. Sixth International Workshop on Pattern Recognition, 25–27 June 2021 Beijing, China. Proceedings of SPIE, the International Society for Optical Engineering, 11913: pp. 119130N. https://dx.doi.org/0.1117/12.2605058
In: Jiang, X. et al. (2021). Sixth International Workshop on Pattern Recognition, 25–27 June 2021 Beijing, China. Proceedings of SPIE, the International Society for Optical Engineering, 11913. SPIE: Washington. , more
In: Proceedings of SPIE, the International Society for Optical Engineering. SPIE: Bellingham, WA. ISSN 0277-786X; e-ISSN 1996-756X, more
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| Keyword |
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| Author keywords |
Geo-tagging. Convolutional Neural Network, Image Processing, RCNN, Object Detection, Object ldentification |
| Authors | | Top |
- Balbin, J.R.
- Sejera, M.M.
- Al-Sagheer, Z.N.
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- Castañeda, J.A.N.B.
- Francisco, V.A.V.
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| Abstract |
Water is the essence of life, and water pollution is a major threat to all living things on this planet. To provide solutions to help combat water pollution, we have created a device that would help locate and identify the different garbage types underwater. This paper focused on the detection and identification of cans, plastics, polystyrenes, and glass underwaterusing object detection and object identification by Convolutional Neural Network and Geotagging. The system set-up comprises the following: a webcam, power bank, Raspberry Pi , GPS module, and an improvise floater. The GUI will display the camera's captured video, the number of garbage identified, and its location in coordinates. The testing was done in two ways: different water visibility and different water levels. The identification accuracy of our program is 94.33% for plastics, 97.34% for glass, 96.89% for polystyrenes, 98.22% for cans, and 96.88% for random garbage, reliability for identification is I 00% for plastics, 91.67% for glass, 91.67% for polystyrenes, 95.83% for cans, and 91.67% for random garbage. The mean, median, and mode for the visibility level s are 96.375, 98, and 99, and the depth level is 96.385, 98. and 99. |
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