AO-UOD: A Novel Paradigm for Underwater Object Detection Using Acousto–Optic Fusion

Abstract

Autonomous underwater vehicles can carry multiple sensors, such as optical cameras and sonars, providing a common platform for underwater multimodal object detection. High-resolution optical images contain color information but are not applicable to turbid water environments. In contrast, acoustical waves are highly penetrating and travel long distances, making them suitable for low-light, turbid underwater environments, but sonar imaging has low resolution. The combination of the two can play to their respective advantages. This article presents a novel paradigm for underwater object detection using acousto–optic fusion (AO-UOD). Given that there is no publicly available data set, this article first constructs a paired data set for fusing optical and sonar images for underwater object detection. Paired sonar images and optical images were acquired by aligning the simulated plane of the ocean bottom. Based on this, a dual-stream interactive object detection network is designed. The network utilizes the structures of the fusion backbone, dual neck, and dual head to establish cross-modal information interaction between acoustical and optical. The attention interactive twin-branch fusion module is used to realize the fusion between features. Experimental results on the data collected show that AO-UOD can effectively fuse optical and sonar images to achieve robust detection performance. The multimodal method can utilize more information and possesses greater advantages over the unimodal method. This research not only provides a solid theoretical foundation for future multimodal object detection in marine environments but also points out the direction of technology development in practical applications.