SRAD: A Multibeam Acoustic Dataset for Submerged Reef Detection in a Laboratory-Recreated Seafloor Environment

Abstract

Multibeam sonar is an important sensing modality for submerged reef detection, with potential applications in ROV operations, navigation safety, and offshore engineering. However, dedicated public acoustic datasets with object-level annotations for submerged reef or reef-like target detection remain limited, restricting the development and evaluation of relevant algorithms. To address this gap, we present SRAD, a multibeam acoustic dataset for submerged reef detection in a laboratory-recreated seafloor environment. The dataset contains more than 3,650 8-bit grayscale sonar images with corresponding ground-truth bounding-box annotations, together with native Oculus sonar logs and supporting data-processing tools. Data were collected in a controlled basin within a large-scale ocean engineering laboratory, where seafloor-related materials, including sand, gravel, natural rock reefs, algal reef materials, shell reefs, and simulated coral structures, were physically arranged to recreate a laboratory seafloor environment. To improve data consistency, an Oculus M750d multibeam sonar mounted on an ROV was used under consistent acquisition settings, and the ground-truth annotations were subjected to expert review and additional consistency checks. The resulting dataset provides labeled multibeam sonar images for the development, validation, and testing of submerged reef detection methods under controlled laboratory conditions, while the native Oculus sonar records and supporting processing tools enable further acoustic-data analysis and reuse.