Development of fish spatio-temporal identifying technology using SegNet in aquaculture net cages

Abstract In marine aquaculture, fish populations constantly decrease throughout the cultivation period because of mortality and escape. Current production management systems provide limited opportunities to count the cultured fish, making it difficult to estimate accurately the fish population in the cage. To overcome this problem, an automatic fish identifying method based on particle tracking velocimetry (PTV) flow visualization technology is proposed in this paper. The proposed method utilizes an image processing unit that extracts individual fish from the acquired image and a motion analysis unit that calculates the motion vector for each individual. Thus, the accuracy of the extraction results in the image processing unit affects the system’s counting results. To validate the efficiency and robustness of the image extraction performed by the image processing unit, individuals were extracted from images using the open-source image deep learning semantic segmentation method (SegNet), which is able to distinguish between the background and foreground in the images via analysis at the pixel level. SegNet is able to improve the image discrimination performance by multiplying the learning paths, and the robustness of the detection results can be ensured by changing the layer structure according to the detection target. Accordingly, the use of SegNet was evaluated in terms of the number of layers and images in the training set. The results of this study indicate that the application of SegNet with PTV technology represents a promising method for the automatic identifying and behavioral tracking of fish in an aquaculture net cage.