论文标题
带有多束前向前的声纳的数据集用于水下对象检测
A Dataset with Multibeam Forward-Looking Sonar for Underwater Object Detection
论文作者
论文摘要
多层前瞻性声纳（MFL）在水下检测中起着重要作用。关于使用MFLS检测水下对象检测的研究面临一些挑战。首先，研究是缺乏可用的数据集。其次，声纳图像通常以像素级别处理，并转化为人类视觉习惯的部门表示，对人工智能（AI）地区的研究不利。面对这些挑战，我们提出了一个新颖的数据集，即水下声学目标检测（UATD）数据集，该数据集由使用Tritech Gemini 1200ik Sonar捕获的9000多个MFLS图像组成。我们的数据集提供了声纳图像的原始数据，并注释了10类目标对象（立方体，圆柱，轮胎等）的注释。数据是从湖水和浅水中收集的。为了验证UATD的实用性，我们将数据集应用于最先进的检测器，并为其准确性和效率提供相应的基准。
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.
