Journal
ICES JOURNAL OF MARINE SCIENCE
Volume 75, Issue 1, Pages 374-389Publisher
OXFORD UNIV PRESS
DOI: 10.1093/icesjms/fsx109
Keywords
deep learning; fish classification; fisheries management; neural networks; stock assessment; underwater video
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Funding
- Australian Research Council from a UWA Research Collaboration Award (RCA) grant [LP110201008]
- Higher Education Commission Pakistan Startup Research Grant [21-949/SRGP/RD/HEC/2016]
- King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research
- Australian Research Council Fellowship [DP110102399]
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There is a need for automatic systems that can reliably detect, track and classify fish and other marine species in underwater videos without human intervention. Conventional computer vision techniques do not perform well in underwater conditions where the background is complex and the shape and textural features of fish are subtle. Data-driven classification models like neural networks require a huge amount of labelled data, otherwise they tend to over-fit to the training data and fail on unseen test data which is not involved in training. We present a state-of-the-art computer vision method for fine-grained fish species classification based on deep learning techniques. A cross-layer pooling algorithm using a pre-trained Convolutional Neural Network as a generalized feature detector is proposed, thus avoiding the need for a large amount of training data. Classification on test data is performed by a SVM on the features computed through the proposed method, resulting in classification accuracy of 94.3% for fish species from typical underwater video imagery captured off the coast of Western Australia. This research advocates that the development of automated classification systems which can identify fish from underwater video imagery is feasible and a cost-effective alternative to manual identification by humans.
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