Assessing fish abundance from underwater video using deep neural networks

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Assessing fish abundance from underwater video using deep neural networks

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Title: Assessing fish abundance from underwater video using deep neural networks
Author/Creator: Mandal, Ranju | Connolly, Rod M | Schlacher, T | Stantic, Bela
Description: Uses of underwater videos to assess diversity and abundance of fish are being rapidly adopted by marine biologists. Manual processing of videos for quantification by human analysts is time and labour intensive. Automatic processing of videos can be employed to achieve the objectives in a cost and time-efficient way. However, there are many challenges involved in this task (e.g. complex background, deformation, low resolution and light propagation). Recent advancement in the deep neural network has led to the development of object detection and recognition in real time scenarios. An end-to-end deep learning-based architecture is introduced which outperformed the state of the art methods and first of its kind on fish assessment task. A Region Proposal Network (RPN) introduced by an object detector termed as Faster R-CNN was combined with three classification networks for detection and recognition of fish species obtained from Remote Underwater Video Stations(RUVS). An accuracy of 82.4% (mAP) obtained from the experiments are much higher than previously proposed methods.
PDF: Assessing fish abundance from underwater video using deep neural networks.
Relation: 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil 8-13 July 2018
Relation: Proceedings of the 2018 International Joint Conference on Neural Networks / Article No. 8489482, pp.1489-1494
Relation: http://dx.doi.org/10.1109/IJCNN.2018.8489482
Year: 2018
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Subject: FoR 0801 (Artificial Intelligence and Image Processing) | deep learning | CNN | underwater video | object detection | classification | marine ecosystem analysis

Resource Type: Conference Paper
Identifier: ISBN: 9781509060146
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