Photographic images sets collecting system and approaches to training neural networks for digital management practical problems solving for a high-performance aquafarm based on the principles of closed water circulation

A.A. Agarkov, K.A. Samsonov, V.N. Malyshev, G.V. Sverdlik

Abstract


The purpose of this work was to develop basic technologies for creating a full-fledged industrial robot in the field of aquabiotechnology for high-intensity fish farms with closed water circulation systems. A controlled robot movement system has been designed, manufactured and tested, as well as a system for collecting sets of video datasets for training neural networks in order to make technological decisions. The integrated control complex of the robotic platform is based on the microcomputer-microcontroller-driver architecture. The system provides positioning of the platform with an accuracy of 5 centimeters and the transmission of a 4K video image with a delay of 150 milliseconds. The mechanical scheme of the robot movement system requires refinement based on the test results to ensure stable movement on curved sections of the trajectory. The layout of the datasets will be carried out by qualified personnel. Based on accumulated cognitive experience. The first version of the neural network has been selected and tested, which successfully solves the problem of determining the size of objects in images in conditions of high concentration and significant mutual overlap. The YOLOv8 convolutional neural network model was used to solve the problem. On the simplest sets of objects, the accuracy of determining the size varies between 97.8 – 99.2%. A set of real video images from an industrial site was obtained for testing the method on real data.

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