Underwater Fish length detection Using the AI-Based Depth Estimation
Open Access
Article
Conference Proceedings
Authors: Ji Yeon Kim, Ki Hwan Kim, Young Jin Kang, Seok Chan Jeong
Abstract: The scale of terrestrial aquaculture is steadily increasing compared to marine aquaculture. It is crucial to automatically observe and manage the growth process in terrestrial aquaculture facilities. However, the need to handle fish out of water to measure their size and weight can decrease their market value. This paper proposes the use of cameras to install underwater cameras for the automatic measurement of fish size, utilizing essential distance detection. This method allows for the relative estimation of the distance of fish underwater, where the pixel to meter approach is not feasible. It enables more accurate predictions of fish size by inferring the depth crucial to the pixel to meter calculation.
Keywords: Underwater, Fish, Depth estimation, Computer vision, non-contact
DOI: 10.54941/ahfe1005819
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