Abstract
Abstract
Tracking and identifying fish species is crucial to understanding marine ecosystem and its role in the world. In this paper, a cost-effective coral fish detection and identification method is proposed. Using the up-to-date models of Convolutional Neural Network (CNN), this paper is able to analyze an underwater footage and highlight all the detectable fish in color frames, and then identifying the species name among the detectable fish. Fish object detection was employed using Open Images Dataset and Tensorflow Object Detection. The paper further explores CNN with squeeze and excitation for fish classification. The proposed model was evaluated on fish4-knowledge dataset and achieved 100% validation accuracy after 50 epochs, better than AlexNet and RetNet, indicating that the solution is robust and practical. In addition, a dataset for coral fish classification in specific location was built using different sources. The model achieved 100% validation accuracy on the proposed dataset.
Subject
General Physics and Astronomy
Cited by
3 articles.
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