| Summary: | The effects of ongoing climate change have caused a poleward shift in the distribution of species due to the rapidly rising water temperatures. This calls for an immediate need to assess and document the extent of climate change-driven animal migrations occurring in the Arctic waters. However, the extreme climatic conditions and the remoteness of the region makes biomonitoring tedious in the Arctic ecosystem. The present study puts forward a deep learning-based analysis of a large underwater video dataset that was captured from the Arctic region. The dataset was acquired using underwater cameras mounted on custom-made stainless-steel frames. The video footages were collected over a period of 26 days from the Kongsfjorden- Krossfjorden twin Arctic fjords in Svalbard, Norway. The collected data sets were used to train YOLO-based object detection framework (You Only Look Once) for an automated detection of the organisms. The YOLO model employed for the study was found to be very efficient in classifying the underwater images captured from the region. The object detection framework could detect images of Comb jelly, Echinoderm, Sea Anemone and Ulke (Shorthorn sculpin) from the underwater images. The model attained a superior value of Mean Average Precision (mAP), precision, and recall of 99.5%, 99.2%, and 97.4%, respectively.
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