An Approach to Generating the Training Sample for Bottom Objects Recognition Neural Networks

Detection of bottom objects is one of the key tasks of an autonomous underwater vehicle. Neural networks are being increasingly used for solving these problems. Application of neural networks to object recognition is always associated with the problem of training sample generation. The lack of training material is particularly relevant in processing the hydroacoustic and optical sea bottom survey data. The paper describes an approach consisting in generating the simulated data. The generation of a training sample is based on three-dimensional modeling of the bottom surface and objects located on it, and the use of hydroacoustic calculations for each of the bottom monitoring tools. The paper describes the transfer of training for a neural network and presents the results of checking its operation on a test sample of real data.