Search

The outbreak of jellyfish blooms poses a serious threat to human life and marine ecology. Therefore, jellyfish detection techniques have earned great interest. This paper investigates the jellyfish detection and classification algorithm based on optical images and deep learning theory. Firstly, we create a dataset comprising 11,926 images. A MSRCR underwater image enhancement algorithm with fusion is proposed. Finally, an improved YOLOv4-tiny algorithm is proposed by incorporating a CBMA module and optimizing the training method. The results demonstrate that the detection accuracy of the improved algorithm can reach 95.01%, the detection speed is 223FPS, both of which are better than the compared algorithms such as YOLOV4. In summary, our method can accurately and quickly detect jellyfish. The research in this paper lays the foundation for the development of an underwater jellyfish real-time monitoring system., (© 2023. Springer Nature Limited.)

This work aims to explore the characteristics of stochastic fluctuant water pressure acted on the surface of large radial gate, and to investigate the flow-induced vibration response of the whole radial gate structure. The finite element calculation model structure of the radial gate is established by taking a large-scale radial gate as prototype to discuss the hydrodynamic pressure acting on the gate leaf with different opening, analyze the dynamic pressure time curves, and achieve the flow-induced vibration response by deeming hydrodynamic pressure as dynamic load. When taking 10% opening of the radial gate, the results indicate that the hydrodynamic pressure distributed on the arc surface of the radial gate changes with the flow conditions, with the maximum pressure occurred at the center of the lower edge of the gate leaf. One point in the time history curve of fluctuating water pressure can be taken as the dynamic load for the flow-induced vibration analysis. The flow-induced vibration responses at the monitoring point of the radial gate structure show periodic changes in the X, Y, and Z directions. The finite element simulation results agree well with the theoretical calculation results, so reference can be provided for the hydrodynamic pressure testing and the flow-induced vibration response calculations., (© 2022. The Author(s).)

In recent years, jellyfish outbreaks have frequently occurred in offshore areas worldwide, posing a significant threat to the marine fishery, tourism, coastal industry, and personal safety. Effective monitoring of jellyfish is a vital method to solve the above problems. However, the optical detection method for jellyfish is still in the primary stage. Therefore, this paper studies a jellyfish detection method based on convolution neural network theory and digital image processing technology. This paper studies the underwater image preprocessing algorithm because the quality of underwater images directly affects the detection results. The results show that the image quality is better after applying the three algorithms namely prior defogging, adaptive histogram equalization, and multi-scale retinal enhancement, which is more conducive to detection. We establish a data set containing seven species of jellyfishes and fish. A total of 2141 images are included in the data set. The YOLOv3 algorithm is used to detect jellyfish, and its feature extraction network Darknet53 is optimized to ensure it is conducted in real-time. In addition, we introduce label smoothing and cosine annealing learning rate methods during the training process. The experimental results show that the improved algorithms improve the detection accuracy of jellyfish on the premise of ensuring the detection speed. This paper lays a foundation for the construction of an underwater jellyfish optical imaging real-time monitoring system.