U2R: Underwater Ultrasonic Reflection Wave Dataset Toward Pose-Invariant Material Recognition

Mayuka Kono, Yutaro Hirao, Monica Perusquía-Hernández, Naoya Isoyama, Hideaki Uchiyama, Nobuchika Sakata, Jun Takamatsu, Kiyoshi Kiyokawa

Abstract: In underwater environments, the reflected ultrasonic waves from objects generally provide more than just information about their color and shape for object recognition. Previous studies have overlooked the influence of object pose on these wave components. It is crucial to investigate how these poses affect the reflected wave components because object poses can vary widely and are often unpredictable in real-world scenarios. In this work, we introduce a novel dataset comprising reflected wave components collected from objects made of various materials and observed from various angles. We also show the preliminary evaluations on the performance of machine learning-based material classification on object pose. Our results indicate that the accuracy is consistently high (≥ 91%) for known angles but significantly drops (< 60%) when dealing with unknown angles in most cases. Based on these evaluations, we suggest several directions for future research. Our dataset is available at https://github.com/Nyamotaro/U2R.