Active Sonar Image Classification Using Deep Convolutional Neural Network Evolved by Robust Comprehensive Grey Wolf Optimizer.

This paper proposes a deep convolutional neural network (DCNN) to design an accurate active sonar image classifier. In order to have a real-time classifier with low complexity, The LeNet-5 is utilized as the most straightforward deep network with the fewest parameters. For the sake of having a real-time training and test phase, the three fully connected layers are replaced by an extreme learning machine (ELM). However, tuning the ELM's input layer parameters is challenging; therefore, this paper tries to tune them using the grey wolf optimizer (GWO). Contrary to other research works and considering the sonar problem's characteristics, we model the problem as a multimodal function. Therefore, comprehensive learning concepts and a novel constraint-handling technique are exerted on the GWO to address the multimodality and the constraints of the sonar image classification task and to have a robust optimizer. Given the vital role of the reliable dataset in deep learning approaches, in the following, an operational underwater sonar test scenario is designed, and an experimental dataset is generated. The designed model is then benchmarked on two benchmark active sonar datasets. The results are investigated by qualified research with classic DCNN, Block-wise Classifier (BWC), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). The investigation outcomes confirm that the designed model, with an average accuracy of 98.69% and computation time equal to 883.44 s, reports the best accuracy and time complexity among other benchmark models. [ABSTRACT FROM AUTHOR]