Abstract
Coral reefs are home of a great fish biodiversity (approximately 7000 species). This biodiversity is the source of many vital ecosystem services such as protein intakes for local populations, nutrients cycle or regulation of algae abundancy. However, increasing human pressure through over-fishing and global warming is destroying both fish popu-lations and their habitats. In this context, monitoring the coral reef fish biodiversity,abundancy and biomass with precision is one of the major issues for marine ecology. To face the increasing pressure and fast globals changes, such monitoring has to be done at a large sclae, temporally and spatially. Up to date, most of fish underwater census is achieved through diving, during which the diver identify fish species and count them. Such manual census induces many constraints (depth and duration of the dive) and biais due to the diver experience. These biais (mistaking fish species or over/under estimating fish populations) are not quantifiable nor correctable. Today, thanks to the improvement of high resolution, low-cost, underwater cameras, new protocoles are developed to use video census. However, there is not yet a way to automaticaly process these underwater videos.Therefore, the analysis of the videos remains a bottleneck between the data gathering through video census and the analysis of fish communities. During this thesis, we develop-ped automated methods for detection and identification of fish in underwater videos with Deep Learning based algorithm. We work on all aspects of the pipeline, from video acqui-sition, data annotation, to the models and post-processings conception, and models testing. Today, we have gather more than 380,000 images of 300 coral reef species. We developped an identification model who successfully identified 20 of the most common species onMayotte coral reefs with 94% rate of success, and post-processing methods allowing us to decrease the error rate down to 2%. We also developped a detection method allowing us to detect up to 84% of fish individuals in underwater videos.