The manual interpretation of traditional side-scan sonar images has problems such as solid subjectivity, low efficiency, high labor cost, and large time consumption, and the machine learning method requires manual feature selection, which lacks adaptability and robustness. In this paper, we introduce a convolutional neural network method, which can automatically learn features from side-scan sonar submarine aircraft wreckage images and complete classification recognition. Using 48 side-scan sonar submarine aircraft images in the SeabedObjects (ship and aircraft) dataset after preprocessing 1686 images, the convolutional neural network model is trained and tested. The results show that the trained CNN model can accurately identify and classify the side-scan sonar submarine aircraft wreck images with an accuracy of 98.85%, which is highly efficient, accurate and robust, and can effectively improve the recognition and classification level of side-scan sonar submarine aircraft wreckage images.
KEYWORDS: Acoustics, Coastal modeling, Signal to noise ratio, Data modeling, Signal attenuation, Submerged target modeling, Oceanography, Waveguides, Waveguide modes, Water
Noise is an interference in the inversion process. To analyze its influence on the model selection in the inversion, this paper selects a uniform seabed model with a layered structure through simulation and uses the fast field method (FFM) to conduct acoustic field calculate. The transmission loss (TL) calculated by the acoustic field is added to the Gaussian noise as the research object, and the acoustic speed, density and acoustic speed attenuation are the inversion objects. The inversion results show that, after adding noise, the inversion method established in this paper can accurately achieve model selection, and the Root Mean Square Error (RMSE) within 0.95, it is verified that the inversion method still has strong anti-noise and accuracy under the influence of noise.
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