摘要
为了解决现有计算机视觉方法难以精确表征鱼类多尺度形态特征的问题,本研究以蓝点马鲛为对象,采用双目立体视觉方法构建鱼类三维重建技术,通过人工测量方法进行鱼类三维模型的体尺精度验证。结果显示,以人工测量数据为基准,蓝点马鲛叉长、体高、最大体周长、鳃盖后缘体周长的平均相对误差分别为0.82%、4.47%、3.14%、2.87%;相对线性拟合方法,蓝点马鲛体周长(最大体周长、鳃盖后缘体周长)与叉长、体高的多元线性拟合式(R^(2)=0.826/0.833)预测精度更高。研究表明,双目立体视觉方法能够构建高精细的鱼类三维模型,可为渔业数智化应用提供重要的技术支撑与数据参考。本研究可为鱼类外形三维重建和渔业数字化信息采集提供方法参考。
Fish are an important source of dietary protein for humans,contributing significantly to national food security and public health.The body size of fish holds significant guiding and practical value for both aquaculture and marine fishing industries.With the advancement of computer vision,non-contact measurement methods are gradually replacing traditional labor-intensive manual measurements to acquire fish morphological characteristics.However,current computer vision methods cannot construct complete three-dimensional models of fish,failing to meet the current demand in the aquaculture sector for three-dimensional digital models of fish.this study utilized structured light projection combined with binocular stereovision methods to reconstruct three-dimensional digital models of fish.A fish point cloud acquisition system was designed,incorporating deep learning networks for fish body image segmentation during the acquisition process.Phase-shifting was used to mark the grayscale on the surface of fish bodies,and finally,a binocular stereovision system was employed to reconstruct fish point clouds.The accuracy of the system was validated using the economically important mackerel species(Scomberomorus niphonius).Results indicated that this method could construct fish point cloud models,with relative errors for fork length,body height,maximum body circumference,and posterior gill cover circumference of the reconstructed mackerel models being 0.82%,4.47%,3.14%and 2.87%,respectively.Correlation analysis and fitting of the relationships between body measurements showed that the multivariate linear regression method(R^(2)=0.826/0.833)outperformed linear regression methods.This study provides a methodological reference for digital information collection in the fisheries industry.
作者
徐安康
黄六一
尤鑫星
毕春伟
何舒玥
徐鑫乐
王笑
XU Ankang;HUANG Liuyi;YOU Xinxing;BI Chunwei;HE Shuyue;XU Xinle;WANG Xiao(College of Fisheries,Ocean University of China,Qingdao 266003,China)
出处
《水产学报》
CAS
CSCD
北大核心
2024年第12期51-60,共10页
Journal of Fisheries of China
基金
国家重点研发计划(2023YFD2401301)
山东省重点研发计划(2021SFGCO701)
青岛市科技计划(23-1-3-hysf-2-hy)。
关键词
蓝点马鲛
鱼类三维重建
深度学习
体尺测量
Scomberomorus niphonius
fish 3D reconstruction
deep learning
fish body measurement
