In order to accurately estimate the distribution of Trachinotus ovatus in marine cages, a novel method was developed using omnidirectional scanning sonar and deep-learning techniques. This method involved differentiating water layers and clustering data layer by layer to achieve precise location estimation. The approach comprised two main components: fish identification and fish clustering. Firstly, omnidirectional scanning sonar was employed to perform spiral detection within marine cages, capturing fish image data. These images were then labeled to construct a training dataset for an enhanced CS-YOLOv8s model. After training, the CS-YOLOv8s model was used to identify and locate fish within the images. Secondly, the cages were divided into water layers with depth intervals of 40 cm. The identification coordinate data for each water layer were clustered using the DBSCAN method to generate location coordinates for the fish in each layer. Finally, the coordinate data from all water layers were consolidated to determine the overall distribution of fish within the cage. This method was shown, through multiple experimental results, to effectively estimate the distribution of Trachinotus ovatus in marine cages, closely matching the distributions detected manually.

In order to reveal the mechanism and influencing factors of high pressure jet cavitation of nozzle in submerged environment, this study focused on the evolutionary process of cavitation bubbles and combined finite volume method and mixed multi-phase flow model to analyze the cavitation, velocity distribution and experimental cavitation intensity of fishing net cleaning equipment. Results show that the cavitation inception, growth and collapse mainly occurred in the peripheral region of the flow field. Ring-shaped cavitation erosion zone appeared on the test sample target. A lot of small dense erosion pits were densely distributed in the ring-shaped erosion zone, erosion marks were observed in the center. The cavitation erosion intensity was greatly affected by the nozzle structure. As the diameter of nozzle increased from 0.6 mm to 1 mm, the maximum gas volume fraction increased by 8.53%. The nozzle outlet enlargement angle greatly increased the cavitation intensity. The nozzle with an outlet angle of 30° exhibited the optimal cavitation erosion performance . The cavitation volume fraction of the nozzle with short necking structure was slightly larger than that of the nozzle with long necking structure at the same level in the necking length rang of 3mm to 7mm. In terms of the influence of nozzle structure on the cavitation erosion effect, the nozzle diameter D ranked the first, followed by the outlet angle α, and the necking length L was at the last.