Imaging-sonar-based Underwater Object Recognition Utilizing Object’s Yaw Angle Estimation with Deep Learning

Abstract This paper proposes a method to recognize underwater target objects and estimate their yaw angle using an imaging sonar. First, a light sonar simulator generated template images of the target objects from various viewing angles. Next, a generative adversarial network predicted a semantic map by segmenting the real sonar image for reliable recognition. Then, matching the template images and semantic map identifies the target object and its yaw angle. We verified the proposed method by installing objects in the indoor water tank. The proposed method can provide relative pose information of sensing platforms which is useful for pose control and navigation.