AUTOMATIC FISH DETECTION FROM DIFFERENT MARINE ENVIRONMENTS VIDEO USING DEEP LEARNING

Loulidi, A.; Houssa, R.; Buhl-Mortensen, L.; Zidane, H.; Rhinane, H.

The marine environment provides many ecosystems that support habitats biodiversity. Benthic habitats and fish species associations are investigated using underwater gears to secure and manage these marine ecosystems in a sustainable manner. The current study evaluates the possibility of using deep learning methods in particular the You Only Look Once version 3 algorithm to detect fish in different environments such as; different shading, low light, and high noise within images and by each frame within an underwater video, recorded in the Atlantic Coast of Morocco. The training dataset was collected from Open Images Dataset V6, a total of 1295 Fish images were captured and split into a training set and a test set. An optimization approach was applied to the YOLOv3 algorithm which is data augmentation transformation to provide more learning samples. The mean average precision (mAP) metric was applied to measure the YOLOv3 model’s performance. Results of this study revealed with a mAP of 91,3% the proposed method is proved to have the capability of detecting fish species in different natural marine environments also it has the potential to be applied to detect other underwater species and substratum.

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Loulidi, A. / Houssa, R. / Buhl-Mortensen, L. / et al: AUTOMATIC FISH DETECTION FROM DIFFERENT MARINE ENVIRONMENTS VIDEO USING DEEP LEARNING. 2022. Copernicus Publications.

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