基于掩模区域的卷积神经网络估计鱼的长度

2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE) Pub Date : 2022-10-28 DOI:10.1109/ECICE55674.2022.10042877

Tzu-Yuan Su, W. W. Hsu, R. Hu, Chia-Chang Tsou, Chun-Han Lin, Wei-Siang Hong

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引用次数: 0

摘要

对鱼的生长和生物行为进行了广泛的研究，这两者都需要对鱼的样本进行测量。然而，现有的测量方法耗时且费力。在本研究中，我们开发了一种基于机器视觉和人工智能的更快的方法来自动测量鱼类的大小和长度，为未来的生态研究提供支持。

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Estimating Fish Length Using Mask Region-Based Convolutional Neural Networks

Extensive research has been conducted on the growth and the biological behaviours which both require the measurement of the fish samples. However, the existing measurement methods were time-consuming and laborintensive. In this research, we developed a faster method based on machine vision and artificial intelligence to measure the fish size and length automatically to support future ecological research.

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2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)

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