A Multimodal Acousto-Optic Dataset for Underwater Image Enhancement, Detection, and Reconstruction

Abstract With the development of acoustic and optical exploration technologies, devices such as optical camera, laser scanner and multibeam sonar provide the possibility of underwater accurate perception. The optimization of acoustic and optical data through algorithms have become one of the major concerns in underwater computer vision field. However, these optimization and improvement algorithms require a large amount of underwater multimodal data for training and evaluation. To address these needs, we propose a multimodal acousto-optic dataset for underwater image enhancement, detection, and reconstruction (MAOUD), which was collected from our underwater simulation environment and includes optical RGB images, acoustic images, labeled images, laser point clouds and acoustic videos. To ensure the accuracy of the dataset, we used a state-of-the-art underwater multimodal integrated detector with guaranteed corresponding kinematic parameters. The dataset can be used for training and evaluation work for a variety of underwater acoustic and optical tasks, serving as a standardized training and validation benchmark for multimodal underwater acoustic and optical algorithms, which holds significant importance for advancing underwater exploration technology.