A comparison of the classification performance of shallow and deep convolutional neural networks in small active sonar datasetsGeunhwan Kim, Youngsang Hwang, Sungjin Shin, Myoungin Shin, Jongkwon Choi, Keunhwa Lee, Juho Kim, Youngmin Choo
An active sonar system transmits and receives a pulse with a short duration to detect and track underwater targets. The detection shrinks candidates for the targets with multiple contacts; some of them are the actual targets. Classification is conducted to find the targets among the contacts. Previous classification studies have used conventional machine learning techniques including support vector machines and displayed limited performance. Due to the recent remarkable development of deep learning using deep neural networks, it is being introduced to active sonar classification. However, superior performance of deep learning is guaranteed when big data are available. In the active sonar classification, deep learning-based classification performance may deteriorate due to the small active sonar dataset, and shallow networks could be alternative. Here, we compare and analyze the classification performance of shallow and deep convolutional neural networks using in-situ active sonar datasets.