Automatic measurement of morphological indexes of three <i>Thunnus</i> species based on computer vision

Ou Liguo; Wang Bingyan; Liu Bilin; Chen Xinjun; Chen Yong; Wu Feng; Liu Pan

doi:10.12284/hyxb2021140

Volume 43 Issue 11

Dec. 2021

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Ou Liguo，Wang Bingyan，Liu Bilin, et al. Automatic measurement of morphological indexes of three Thunnus species based on computer vision[J]. Haiyang Xuebao，2021, 43(11)：105–115 doi: 10.12284/hyxb2021140

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Ou Liguo，Wang Bingyan，Liu Bilin, et al. Automatic measurement of morphological indexes of three Thunnus species based on computer vision[J]. Haiyang Xuebao，2021, 43(11)：105–115 doi: 10.12284/hyxb2021140

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Automatic measurement of morphological indexes of three Thunnus species based on computer vision

doi: 10.12284/hyxb2021140

Ou Liguo^1
,,
Wang Bingyan²,
Liu Bilin^{1, 3, 4, 5, 6
,
,},
Chen Xinjun^{1, 3, 4, 5, 6},
Chen Yong¹,
Wu Feng¹,
Liu Pan¹

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
2.
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
3.
Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China
4.
National Engineering Research Center for Oceanic Fisheries, Shanghai 201306, China
5.
Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China
6.
Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China

Received Date: 2021-03-12
Rev Recd Date: 2021-06-17

Available Online: 2021-06-22

Publish Date: 2021-12-31

Abstract

Abstract

Tuna is an important fishing target in China’s pelagic fishery. Its morphological indexes are of great significance for the study of the growth, development and life history of tunas. Manual measurement of morphological index is a very tedious and inefficient measurement method, while computer vision is an efficient and objective automatic measurement method. Therefore, in this paper, images of three Thunnus species are preprocessed by the computer vision library (OpenCV). It mainly uses image processing techniques such as bilateral filter, gray transformation, image binarization and contour extraction to obtain the contour image of tuna. According to the pre-selected feature points, the computer vision technology is used to traversal all the pixel points on the contour image, and 17 pre-selected feature points of each contour image are automatically located. By using the computer vision technology, the pixel length of the morphological index of the three species of tuna is automatically measured and the actual length of the morphological index is calculated. The absolute error and relative error between automatic measurement and manual measurement are compared and analyzed. The results show that the computer vision technique is effective in the automatic measurement of the morphological indexes of the three Thunnus species. The absolute error ranges of 12 morphological indices of Thunnus obesus, Thunnus albacores and Thunnus alalunga are 0.00−1.46 cm, 0−1.73 cm and 0−1.32 cm, respectively, and the relative error ranges are 0.01%−5.84%, 0%−6.17% and 0%−6.89%, respectively. It is expected to provide a basis for intelligent identification of tuna and a basic reference for automatic measurement of other fish.
- computer vision,
- Thunnus,
- morphological contour,
- feature points,
- morphological indexes,
- automatic measurement

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References(31)

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