Real-time Weakly Supervised Semantic Segmentation of Seabed Sediments in Side-scan Sonar Images
Real-time Weakly Supervised Semantic Segmentation of Seabed Sediments in Side-scan Sonar Images Distinguishing between marine benthic habitat characteristics is of key importance in a wide array of applications from installations of oil rigs to laying networks of cables and monitoring the impact of...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/26773 |
| Acceso en línea: | http://hdl.handle.net/10256/26773 |
| Access Level: | acceso abierto |
| Palabra clave: | Imatges -- Segmentació Image segmentation Seafloor Fons marí Robòtica Robotics |
| Sumario: | Real-time Weakly Supervised Semantic Segmentation of Seabed Sediments in Side-scan Sonar Images Distinguishing between marine benthic habitat characteristics is of key importance in a wide array of applications from installations of oil rigs to laying networks of cables and monitoring the impact of humans on marine ecosystems. The Side Scan Sonar (SSS) is a widely used sensor in this regard. It works on the principle of acoustic propagation and reflection to produce high-resolution images by logging the intensities of sound waves reflected back from the seafloor. The goal of this work would be to leverage these acoustic intensity maps to produce pixel-wise categorization of different seafloor types. The annotations of the seafloor used to supervise model training were somewhat noisy. This results from the fact that the annotations were made on SSS mosaics while we are working with raw SSS waterfalls. Transferring these annotations to raw waterfalls is not a straightforward process, especially without having access to the internal parameters used for mosaicing and thus, leads to certain discrepancies. Therefore, the ground truth generated for the raw waterfalls is not pixel-wise accurate and the trained models suffer from weak supervision. Therefore, we plan to adopt a weakly supervised learning framework to achieve our goal of seabed segmentation. We further plan to supplement the framework by leveraging the noisy ground truth that we have available acting as pseudo masks to regularize training. Steps to be done: ● The structure of available data should be understood ● Selection of two best approach to implement ● Two best approach should be working, tuned and trained (loss converging) on remote server (Falcon) ● Comparison of performance of the two approach (speed, IoU) ● Comparison with fully supervised approach (chosen baseline) keywords: Seafloor Segmentation, side-scan sonar, Weakly supervised approach, real-time. |
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