Computer Vision for Marine Environmental Monitoring

Jonas Osterloff
2018
Ocean exploration using imaging techniques has recently become very popular as camera systems became affordable and technique developed further. Marine imaging provides a unique opportunity to monitor the marine environment. The visual exploration using images allows to explore the variety of fauna, flora and geological structures of the marine environment. This monitoring creates a bottleneck as a manual evaluation of the large amounts of underwater image data is very time consuming.
more » ... n encapsulated in the images need to be extracted so that they can be included in statistical analyzes. Objects of interest (OOI) have to be localized and identified in the recorded images. In order to overcome the bottleneck, computer vision (CV) is applied in this thesis to extract the image information (semi-) automatically. A pre-evaluation of the images by marking OOIs manually, i.e. the manual annotation process, is necessary to provide examples for the applied CV methods. Five major challenges are identified in this thesis to apply of CV for marine environmental monitoring. The challenges can be grouped into challenges caused by underwater image acquisition and by the use of manual annotations for machine learning (ML). The image acquisition challenges are the optical properties challenge, e.g. a wavelength dependent attenuation underwater, and the dynamics of these properties, as different amount of matter in the water column affect colors and illumination in the images. The manual annotation challenges for applying ML for underwater images are, the low number of available manual annotations, the quality of the annotations in terms of correctness and reproducibility and the spatial uncertainty of them. The latter is caused by allowing a spatial uncertainty to speed up the manual annotation process e.g. using point annotations instead of fully outlining OOIs on a pixel level. The challenges are resolved individually in four different new CV approaches. The individual CV approaches allo [...]
doi:10.4119/unibi/2931281 fatcat:v6fvwizykrcypha4i66zqkyfdy