Bilinear Pooling With Poisoning Detection Module for Automatic Side Scan Sonar Data Analysis

Abstract

Side-scan sonar (SSS) images are difficult for automatic analysis due to the acoustic measurement parameters as well as the number of different objects that can be distant. In addition, there is a risk that the seabed analysis application may be attacked. For this purpose, we propose a solution based on convolutional neural networks with bilinear pooling in order to achieve higher values of classification accuracy. Bilinear pooling merge data from two networks and return classification results. The first network’s branch receives the original image and the second one after applying the superpixel method. This approach allows to focus on different types of features. In addition, we introduced a mechanism of poisoning detection that analyze images and results from the network. For the evaluation process, we used the real SSS images obtained between two water channels in Szczecin city in north-western Poland. The importance of scientific research indicates the accuracy of the analysis as well as the safety of the measurements performed.

Keywords

• Classification
• convolutional neural networks
• machine learning
• poisoning detection
• side-scan sonar images