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Automatic measurement of morphological indexes of three Thunnus species based on computer vision
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Liguo Ou1, Bingyan Wang2, Bilin Liu1, 3, 4, 5, 6, *, Xinjun Chen1, 3, 4, 5, 6, Yong Chen1, Feng Wu1, Pan Liu1
Haiyang Xuebao | 2021, 43(11) : 105 - 115
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Haiyang Xuebao | 2021, 43(11): 105-115
Article
Automatic measurement of morphological indexes of three Thunnus species based on computer vision
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Liguo Ou1, Bingyan Wang2, Bilin Liu1, 3, 4, 5, 6, *, Xinjun Chen1, 3, 4, 5, 6, Yong Chen1, Feng Wu1, Pan Liu1
Affiliations
  • 1College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
  • 2College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
  • 3Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China
  • 4National Engineering Research Center for Oceanic Fisheries, Shanghai 201306, China
  • 5Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China
  • 6Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China
Published: 2021-11-25 doi: 10.12284/hyxb2021140
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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
Liguo Ou, Bingyan Wang, Bilin Liu, Xinjun Chen, Yong Chen, Feng Wu, Pan Liu. 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
Year 2021 volume 43 Issue 11
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Article Info
doi: 10.12284/hyxb2021140
  • Receive Date:2021-03-12
  • Online Date:2026-02-26
  • Published:2021-11-25
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History
  • Received:2021-03-12
  • Revised:2021-06-17
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Affiliations
    1College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    2College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
    3Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China
    4National Engineering Research Center for Oceanic Fisheries, Shanghai 201306, China
    5Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China
    6Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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