  Multibeam forward-looking sonar (MFLS) plays an important role in underwater
detection. There are several challenges to the research on underwater object
detection with MFLS. Firstly, the research is lack of available dataset.
Secondly, the sonar image, generally processed at pixel level and transformed
to sector representation for the visual habits of human beings, is
disadvantageous to the research in artificial intelligence (AI) areas. Towards
these challenges, we present a novel dataset, the underwater acoustic target
detection (UATD) dataset, consisting of over 9000 MFLS images captured using
Tritech Gemini 1200ik sonar. Our dataset provides raw data of sonar images with
annotation of 10 categories of target objects (cube, cylinder, tyres, etc). The
data was collected from lake and shallow water. To verify the practicality of
UATD, we apply the dataset to the state-of-the-art detectors and provide
corresponding benchmarks for its accuracy and efficiency.
