Method for the multi-view estimation of fish mass using a two-stage neural network with edge-sensitive module
Keywords:
fish mass, multi-view estimation, two-stage neural network, edge-sensitive module, image segmentationAbstract
The estimation of fish mass is one of the most basic and important tasks in aquaculture. Acquiring the mass of fish at different growth stages is of great significance for feeding, monitoring the health status of fish, and making breeding plans to increase production. The existing estimation methods for fish mass often stay in the 2D plane, and it is difficult to obtain the 3D information on fish, which will lead to the error. To solve this problem, a multi-view method was proposed to obtain the 3D information of fish and predict the mass of fish through a two-stage neural network with an edge-sensitive module. In the first stage, the side- and downward-view images of the fish and some 3D information, such as side area, top area, length, deflection angle, and pitch angle, were captured to estimate the size of the fish through two vertically placed cameras. Then the area of the fish at different views was estimated accurately through the pre-trained image segmentation neural network with an edge-sensitive module. In the second stage, a fully connected neural network was constructed to regress the fish mass based on the 3D information obtained in the previous stage. The experimental results indicate that the proposed method can accurately estimate the fish mass and outperform the existing estimation methods. Keywords: fish mass, multi-view estimation, two-stage neural network, edge-sensitive module, image segmentation DOI: 10.25165/j.ijabe.20241703.6840 Citation: Jiao Z Y, Cai Y J, Zhang Q, Zhong Z Y. Method for the multi-view estimation of fish mass using a two-stage neural network with edge-sensitive module. Int J Agric & Biol Eng, 2024; 17(3): 222-229.References
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[2] Zion B. The use of computer vision technologies in aquaculture - A review. Computers and Electronics in Agriculture, 2012; 88: 125–132.
[3] Zhang L, Wang J P, Duan Q L. Estimation for fish mass using image analysis and neural network. Computers and Electronics in Agriculture, 2020; 173: 105439.
[4] Fulton T W. The rate of growth of fishes. Twenty-second Annual Report, 1904; 3(3): 326–446.
[5] Sanchez-Torres G, Ceballos-Arroyo A, Robles-Serrano S. Automatic measurement of fish weight and size by processing underwater hatchery images. Engineering Letters, 2018; 26(4): 461–472.
[6] Froese R, Tsikliras A C, Stergiou K I. Editorial note on weight-length relations of fishes. Acta Ichthyologica et Piscatoria, 2011; 41(4): 261–263
[7] Froese R, Thorson J T, Reyes R B. A Bayesian approach for estimating length-weight relationships in fishes. Journal of Applied Ichthyology, 2013; 30(1): 78–85.
[8] Venerus L A, Villanueva Gomila G L, Sueiro M C, Bovcon N D. Length-weight relationships for two abundant rocky reef fishes from northern Patagonia, Argentina: Sebastes oculatus Valenciennes, 1833 and Pinguipes brasilianus Cuvier, 1829. Journal of Applied Ichthyology, 2016; 32(6): 1347–1349.
[9] Hufschmied P, Fankhauser T, Pugovkin D. Automatic stress-free sorting of sturgeons inside culture tanks using image processing. Journal of Applied Ichthyology, 2011; 27(2): 622–626.
[10] Gümüş B, Balaban M O. Prediction of the weight of aquacultured rainbow trout (Oncorhynchus mykiss) by image analysis. Journal of Aquatic Food Product Technology, 2010; 19(3-4): 227–237.
[11] Al-Jubouri Q, Al-Nuaimy W, Al-Taee M, Young I. An automated vision system for measurement of zebrafish length using low-cost orthogonal web cameras. Aquacultural Engineering, 2017; 78(PartB): 155–162.
[12] Miranda J M, Romero M. A prototype to measure rainbow trout’s length using image processing. Aquacultural Engineering, 2017; 76: 41–49.
[13] Viazzi V, Van Hoestenberghe S, Goddeeris B M, Berckmans D. Automatic mass estimation of Jade perch Scortum barcoo by computer vision. Aquacultural Engineering, 2015; 64: 42–48.
[14] Saberioon M, Císař P. Automated within tank fish mass estimation using infrared reflection system. Computers and Electronics in Agriculture, 2018; 150: 484–492.
[15] de Verdal H, Vandeputte M, Pepey E, Vidal M-O, Chatain B. Individual growth monitoring of European sea bass larvae by image analysis and microsatellite genotyping. Aquaculture, 2014; 434: 470–475.
[16] Ault J S, Luo J G, A reliable game fish weight estimation model for atlantic tarpon (Megalops atlanticus). Fisheries Research, 2013; 139: 110–117.
[17] Costa C, Antonucci F, Boglione C, Menesatti P, Vandeputte M, Chatain B. Automated sorting for size, sex and skeletal anomalies of cultured seabass using external shape analysis. Aquacultural Engineering, 2013; 52: 58–64.
[18] Balaban M O, Chombeau M, Gümüş B, Cirban D. Determination of volume of alaska pollock (Theragra chalcogramma) by image analysis. Journal of Aquatic Food Product Technology, 2011; 20(1): 45–52.
[19] Wang W J, Xu J Y, Lyu Z M, Xin N H. Weight estimation of underwater Cynoglossus semilaevis based on machine vision. Transactions of the CSAE, 2012; 28(16): 153–157. (in Chinese)
[20] Odone F, Trucco E, Verri A. A trainable system for grading fish from images. Applied Artificial Intelligence, 2001; 15(8): 735–745.
[21] He K M, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision, 2017; pp.2961–2969.
[22] Whitted T. An improved illumination model for shaded display. In: Proceedings of the 6th annual conference on Computer Graphics and Interactive Techniques, 1979; 13(2): 807419.
[23] Kirillov A, Wu Y X, He K M, Girshick R. Pointrend: Image Segmentation as Rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020; pp.9799–9808.
[24] Lin T Y, Dollár P, Girshick R, He K M, Hariharan B, Belongie S. Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017; Honolulu: 2117–2125.
[25] Toussaint G T. Solving geometric problems with the rotating calipers. In: Proceeding of IEEE Melecon’83, Athens: IEEE, 1983; 8p.
[26] Kůrková V. Kolmogorov’s theorem and multilayer neural networks. Neural Networks, 1992; 5(3): 501–506.
[27] Konovalov D A, Saleh A, Efremova D B, Domingos J A, Jerry D R. Automatic weight estimation of harvested fish from images. In: 2019 Digital Image Computing: Techniques and Applications (DICTA), Peth: IEEE, 2019; pp.1–7.
[28] Deng J, Dong W, Socher R, Li L J, Li K, Li F F. Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami: IEEE, 2009; pp.248–255.
[29] Lin T Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick C L. Microsoft CoCo: Common objects in context. In: European Conference on Computer Vision (ECCV 2014), Springer, 2014; pp.740–755.
[30] Hoiem S K, Divvala J H. Hays, Pascal VOC 2008 challenge. In: PASCAL Challenge Workshop in ECCV, Citeseer, 2009.
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Published
2024-07-11
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Jiao, Z., Cai, Y., Zhang, Q., & Zhong, Z. (2024). Method for the multi-view estimation of fish mass using a two-stage neural network with edge-sensitive module. International Journal of Agricultural and Biological Engineering, 17(3), 222–229. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/6840
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Information Technology, Sensors and Control Systems
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