The advancement of foundation models have opened up new possibilities in deep learning. These models can be adapted to new tasks and unseen domains from minimal or even no training data, making them well-suited for applications where labelled data is scarce or costly to collect. Lack of data has meant that deep learning for change detection in sonar imagery has not been used. Reliable methods for change detection of underwater environments is critical for a range of fields such as marine research and object search. Previous work in change detection for sonar imagery focus on non-deep learning methods. In this paper, we explore the use of a foundation model (Segment Anything Model) for performing change detection in imagery collected with synthetic aperture sonar (SAS). This thesis is the first case of applying Segment Anything Model to change detection in SAS imagery. The proposed method segments bi-temporal images, and change detection is then performed on the segments. In addition to a set of bi-temporal images containing real change, the model is also tested on a set of synthetic images. The proposed method shows promising results on both a real and synthetic data set.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-205517 |
| Date | January 2024 |
| Creators | Hedlund, William |
| Publisher | Linköpings universitet, Datorseende |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0129 seconds