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Underwater Fish Body Length Estimation Based on Binocular Image Processing
- Source :
- Information, Vol 11, Iss 476, p 476 (2020), Information, Volume 11, Issue 10
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
Abstract
- Recently, the information analysis technology of underwater has developed rapidly, which is beneficial to underwater resource exploration, underwater aquaculture, etc. Dangerous and laborious manual work is replaced by deep learning-based computer vision technology, which has gradually become the mainstream. The binocular cameras based visual analysis method can not only collect seabed images but also construct the 3D scene information. The parallax of the binocular image was used to calculate the depth information of the underwater object. A binocular camera based refined analysis method for underwater creature body length estimation was constructed. A fully convolutional network (FCN) was used to segment the corresponding underwater object in the image to obtain the object position. A fish&rsquo<br />s body direction estimation algorithm is proposed according to the segmentation image. The semi-global block matching (SGBM) algorithm was used to calculate the depth of the object region and estimate the object body length according to the left and right views of the object. The algorithm has certain advantages in time and accuracy for interest object analysis by the combination of FCN and SGBM. Experiment results show that this method effectively reduces unnecessary information, improves efficiency and accuracy compared to the original SGBM algorithm.
- Subjects :
- 0106 biological sciences
Matching (graph theory)
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image processing
underwater
01 natural sciences
GeneralLiterature_MISCELLANEOUS
SGBM
Computer vision
Segmentation
Underwater
ComputingMethodologies_COMPUTERGRAPHICS
Block (data storage)
fish
body length
lcsh:T58.5-58.64
business.industry
lcsh:Information technology
010604 marine biology & hydrobiology
Deep learning
04 agricultural and veterinary sciences
Object (computer science)
FCN
040102 fisheries
0401 agriculture, forestry, and fisheries
Artificial intelligence
business
Parallax
Information Systems
Subjects
Details
- Language :
- English
- ISSN :
- 20782489
- Volume :
- 11
- Issue :
- 476
- Database :
- OpenAIRE
- Journal :
- Information
- Accession number :
- edsair.doi.dedup.....9aeb148dfe42d4b0c469de6ed982443e