摘要
Research on the high maneuverability of fish swimming primarily involves addressing the batch processing of large experimental data,specifically how to simultaneously capture and rapidly process deformation-displacement information of fish bodies and related flow fields.The primary objective of this study is to integrate high-speed photography technology with deep learning methods to propose a set of data processing methods suitable for extracting fish swimming characteristic parameters.For the rapid movements of zebrafish(millisecond-level motion),this study utilized a high-speed camera for image acquisition,obtaining batches of swimming fish images and fluorescence particle information in the flow field.The geometric reconstruction of zebrafish under high-speed swimming was achieved by introducing deep learning algorithms and refining the U-Net model.To tackle the challenges of complex fish swimming scenes,we utilized a novel residual connection approach(path modification)and constructed a hybrid function model(module enhancement),resulting in a new neural network model tailored for zebrafish swimming image processing:Mod-UNet.Through testing,the improved Mod-UNet model effectively eliminated interference from fluorescence particles in the flow field on the extraction of fish body contours,achieving an overall IoU coefficient of 93%.The processing results demonstrated a kind of consistency compared to results obtained with traditional methods by previous researchers.By calculating the geometric morphology of zebrafish,we further derived the kinematic characteristics of zebrafish.Simultaneously,by applying cross-correlation algorithms to calculate the positions of fluorescence particles,the velocity characteristics of the flow field were obtained.The λ ci method and the Ω method were used to identify vortex structures,providing the evolution patterns of corresponding flow field characteristic parameters.The experimental data processing method proposed in this paper provides technical support for establishing a zebrafish swimming information database.
基金
Project supported by the National Natural Science Foundation of China(Grant No.12172355)
the Fundamental Research Funds for the Central Universities(Grant Nos.E1E42201,E3E42203).
