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Article

Underwater Biological Detection Algorithm Based on Improved Faster-RCNN

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Jiangsu Key Laboratory of Power Transmission & Distribution Equipment Technology, Hohai University, Changzhou 213022, China
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College of Internet of Things Engineering, Hohai University, Changzhou 213022, China
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Authors to whom correspondence should be addressed.
Academic Editor: Gwo-Fong Lin
Water 2021, 13(17), 2420; https://doi.org/10.3390/w13172420
Received: 22 July 2021 / Revised: 30 August 2021 / Accepted: 1 September 2021 / Published: 3 September 2021
(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)
Underwater organisms are an important part of the underwater ecological environment. More and more attention has been paid to the perception of underwater ecological environment by intelligent means, such as machine vision. However, many objective reasons affect the accuracy of underwater biological detection, such as the low-quality image, different sizes or shapes, and overlapping or occlusion of underwater organisms. Therefore, this paper proposes an underwater biological detection algorithm based on improved Faster-RCNN. Firstly, the ResNet is used as the backbone feature extraction network of Faster-RCNN. Then, BiFPN (Bidirectional Feature Pyramid Network) is used to build a ResNet–BiFPN structure which can improve the capability of feature extraction and multi-scale feature fusion. Additionally, EIoU (Effective IoU) is used to replace IoU to reduce the proportion of redundant bounding boxes in the training data. Moreover, K-means++ clustering is used to generate more suitable anchor boxes to improve detection accuracy. Finally, the experimental results show that the detection accuracy of underwater biological detection algorithm based on improved Faster-RCNN on URPC2018 dataset is improved to 88.94%, which is 8.26% higher than Faster-RCNN. The results fully prove the effectiveness of the proposed algorithm. View Full-Text
Keywords: deep learning; object detection; underwater detection deep learning; object detection; underwater detection
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MDPI and ACS Style

Shi, P.; Xu, X.; Ni, J.; Xin, Y.; Huang, W.; Han, S. Underwater Biological Detection Algorithm Based on Improved Faster-RCNN. Water 2021, 13, 2420. https://doi.org/10.3390/w13172420

AMA Style

Shi P, Xu X, Ni J, Xin Y, Huang W, Han S. Underwater Biological Detection Algorithm Based on Improved Faster-RCNN. Water. 2021; 13(17):2420. https://doi.org/10.3390/w13172420

Chicago/Turabian Style

Shi, Pengfei, Xiwang Xu, Jianjun Ni, Yuanxue Xin, Weisheng Huang, and Song Han. 2021. "Underwater Biological Detection Algorithm Based on Improved Faster-RCNN" Water 13, no. 17: 2420. https://doi.org/10.3390/w13172420

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