LAUSR

Side-scan sonar generates an image based on the reflection of sound waves. The reflection takes place with the first encountered object, which, in practice, may be a passing fish or even a wave from the boat turbine. Therefore, such data are sensitive to all noises. In this article, we propose a real-time automatic system of side-scan sonar analysis that can detect and classify different objects. Our proposition is based on the processing of a given image to quickly verify whether there is anything other than the bottom of the river/sea. If so, this image is analyzed in terms of regions of interest (ROIs) by the histogram module. This action allows the extraction of only objects of interest, which are then classified by convolutional neural networks (CNNs). A proposed model also contains an automatic mechanism of adding the sample to the database in order to later guarantee the accuracy of the classifiers. The model is a hybridization of image processing techniques with a machine learning approach to analyze difficult images. The presented system has been tested on the bottom of a river in Szczecin, Poland. We reached 90% of accurate classification in case scenario and 88% in simulation on used datasets. The obtained results were presented and discussed in terms of the advantages of practical application in analyzing side-scan sonar images.