New method of mussel survey by using high-resolution acoustic video camera-ARIS and deep learning
New method of mussel survey by using high-resolution acoustic video camera-ARIS and deep learning
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DOI：
10.1109/oceanschennai45887.2022.9775335
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Fan Zhao;K. Mizuno;S. Tabeta;Takato Asayama;Hiroki Hayami;Y. Fujimoto;T. Shimada
中科院分区：
文献类型：
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作者：
Fan Zhao;K. Mizuno;S. Tabeta;Takato Asayama;Hiroki Hayami;Y. Fujimoto;T. Shimada
由于水体透明度、水深和较高的劳动力需求，传统的水下调查方法（例如光学传感和样方调查）有其局限性。因此，为了克服这些障碍，本文提出了一种声学传感方法，使用高分辨率声学摄像机 ARIS 来可视化湖底并研究贻贝的分布。新的水下传感方法通过图像马赛克操作生成近视频质量的声学图像来构建地图，这有助于评估贻贝的状况。卷积神经网络（CNN）在本研究中展示了其对贻贝检测和分类的帮助。同时，训练有素的深度学习模型的准确性和效率设法提高了这项研究。通过实地调查，该方法成功获得了伊豆沼湖贻贝的分布图。
Due to water transparency, water depth, and higher labor demand, conventional methods for the underwater survey (e.g., optical sensing and quadrat survey) have their limitations. Thus, to overcome these barriers, this paper proposes a method of acoustic sensing which uses the high-resolution acoustic video camera-ARIS to visualize the lake bottom and investigate the distribution of mussels. Newly underwater sensing method produces near-video quality acoustic images for constructing the map by Image Mosaic Operation, which can be helpful for assessing the status of mussels. Convolutional Neural Network(CNN) shows its help in the detection and classification of mussels in this study. Meanwhile, the accuracy and efficiency of the well-trained deep learning model manage to improve this research. Through the field survey, the proposed method successfully obtained the distribution maps of mussels in Lake Izunuma.