Research on underwater coral identification method based on deep learning

Xin-gang Xie; Jia-wen Zeng; Zhao-xuan Lu; Jing-kun Liang

doi:10.1117/12.3034149

13 June 2024 Research on underwater coral identification method based on deep learning

Xin-gang Xie, Jia-wen Zeng, Zhao-xuan Lu, Jing-kun Liang

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131804Q (2024) https://doi.org/10.1117/12.3034149
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Soft corals are an important component of marine ecosystems, which have potential medicinal and medical research value. In order to monitor information such as the coverage and abundance of underwater soft corals, A large number of images are usually acquired by manual detection methods or by using remotely operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs). Then, These images are manually annotated by marine experts or automatically annotated using a Convolutional Neural Network (CNN) classifier. However, Manually annotating a large number of images is both tedious and time-consuming, while CNN classifiers can only classify images in a simple way, which does not adequately represent more important information in the image. To address the challenges in underwater soft coral monitoring, this paper proposes an improved Faster R-CNN detection model aimed at automatically locating and identifying different species of soft corals in an image and providing more detailed annotations. We created a dataset containing common soft coral species in the Sanya sea area through on-site photography. On this dataset, compared with the original Faster R-CNN model, the improved Faster R-CNN model improved 39.7% in mAP and 45.6%in mAP75. In addition, the model also outperforms some newer models.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xin-gang Xie, Jia-wen Zeng, Zhao-xuan Lu, and Jing-kun Liang "Research on underwater coral identification method based on deep learning", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131804Q (13 June 2024); https://doi.org/10.1117/12.3034149

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KEYWORDS

Target recognition

Performance modeling

Deep learning

Object detection

Target detection

Convolution

Image processing

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