Convolutional-Neural-Network-based Underwater Object Detection Using Sonar Image Simulator with Randomized Degradation

This paper proposes a method to detect underwater objects using sonar image simulator and convolutional neural network (CNN). Instead of simulating very realistic sonar images which is computationally complex, we implemented a simple sonar simulator that calculates only semantic information. Then, we generated training images of target objects by adding randomized degradation effects to the simulated images. The CNN trained with these generated images is robust to the degradation effects inherent in sonar images and thus can detect target objects in real sonar images. We verified the proposed method using the sonar images captured at sea through field experiments. The proposed method can implement object detection more easily because it only uses simulated images instead of real sonar images which are challenging to acquire. The proposed method can also be applied to other sonar-image-based algorithms.