In real world testing compared to manual image matching, the algorithm reduces image management time by at least 98% and reduces error rates of missing potential matches from approximately 6–9% to 1–3%. ![]() The algorithm requires minimal image pre-processing and is capable of accurate, rapid matching of fair to high-quality humpback fluke photographs. This offers the potential to successfully automate recognition of individuals in large photographic datasets such as in ocean basin-wide marine mammal studies. The extracted features are then compared against those of the reference set of previously known humpback whales for similarity. The method uses a Densely Connected Convolutional Network (DenseNet) to extract special keypoints of an image of the ventral surface of the fluke and then a separate DenseNet trained to look for features within these keypoints. ![]() We describe the development and application of a new convolutional neural network-based photo-identification algorithm for individual humpback whales ( Megaptera novaeangliae).
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