SWDD: Sonar Wall Detection Dataset

The dataset SWDD: Sonar Wall Detection Dataset is part of the paper Knowledge Distillation in YOLOX-ViT for Side Scan Sonar Object Detection. SWDD has been recorded with a lightweight autonomous underwater vehicle (LAUV), operated by OceanScan-MST and carrying a Klein 3500 side scan sonar (SSS). The AUV has been deployed in the Porto de Leixões harbor following the harbor's walls while collecting SSS raw data. The SSS operated at a high frequency of 900kHz with a range of 75m for a total range of 150m from port to starboard. This setup produced a resolution of 4.168 pixels per line. The data were processed using Neptus software, transforming the raw data into waterfall images. The 216 images have been manually annotated with two different classes, wall and noWall. Data augmentation such as noise, flips, and combined noise-flip transformations have been applied, increasing the data to 864 images. Across the 864 images, there are a total of 2,616 labeled samples. To ensure robust training, with respect to the data quality, the original dataset has been mixed with the augmented images. The dataset is divided into 70% for training, 15% for validation, and 15% for testing. The authors chose to generate images with 500 lines, meaning the images have a resolution of 4.168x500. Finally, the images have been resized to 640 × 640 to use this data in specific computer vision algorithms. The dataset is annotated following the COCO annotation format. YOLOX and YOLOX-ViT have been trained and compared using the SWDD dataset. A 6-minute 57-second video from another survey was used for model comparison. From this video, 6243 frames have been extracted with its manually annotated ground truth. Thus, this dataset repository offers an SSS dataset, a 6-minute 57-second SSS video, and 6243 extracted frames from this video with its manually annotated ground truth. The Knowledge Distillation in YOLOX-ViT code using the SWDD dataset is publicly available at KD-YOLOX-ViT. For (re-)using/publishing SWDD, please include the following copyright text: SWDD is a public dataset collected with a Light Autonomous Underwater Vehicle by Oceanscan-MST, within the scope of the H2020 REMARO project.SWDD数据集：声呐墙体检测数据集（Sonar Wall Detection Dataset）隶属于《面向侧扫声呐目标检测的YOLOX-ViT知识蒸馏》一文。该数据集由OceanScan-MST运营的轻型自主水下航行器（lightweight autonomous underwater vehicle, LAUV）搭载Klein 3500型侧扫声呐（side scan sonar, SSS）采集得到。该自主水下航行器沿莱肖埃斯港（Porto de Leixões）的港壁航行部署，同步采集侧扫声呐原始数据。该侧扫声呐工作主频为900kHz，单侧探测距离达75米，从左舷到右舷的总探测范围为150米。该采集配置的单条扫描线分辨率为4.168像素。采集所得原始数据经Neptus软件处理后，转换为瀑布图格式的声呐图像。初始数据集包含216张经人工标注的图像，标注类别分为墙体（wall）与非墙体（noWall）两类。通过添加噪声、图像翻转以及噪声翻转组合变换等方式完成数据增强，将数据集扩充至864张图像。在全部864张图像中，总计包含2616个标注样本。为保障模型训练的鲁棒性与数据质量，原始数据集与增强后数据集进行了混合整合。该数据集按70%训练集、15%验证集、15%测试集的比例进行划分。数据集构建者将原始图像的扫描线数设定为500条，即图像分辨率为4.168×500。最终，为适配特定计算机视觉算法的输入要求，所有图像均被统一缩放至640×640像素尺寸。该数据集采用COCO标注格式进行标注。研究团队基于SWDD数据集完成了YOLOX与YOLOX-ViT模型的训练与性能对比。此外，研究团队还使用了另一项勘测任务中采集的一段6分57秒的声呐视频用于模型对比实验。从该视频中提取出6243帧图像，并完成了人工标注的真值（ground truth）标注。综上，本数据集仓库不仅提供了SWDD声呐墙体检测数据集，还包含上述6分57秒的侧扫声呐视频，以及从中提取的6243帧带人工标注真值的图像。基于SWDD数据集的YOLOX-ViT知识蒸馏代码已在KD-YOLOX-ViT开源仓库公开。若需二次使用或发布SWDD数据集，请务必包含以下版权声明：SWDD是由Oceanscan-MST在H2020计划REMARO项目框架下，通过轻型自主水下航行器采集的公开数据集。

搜集汇总

背景与挑战

背景概述

SWDD是一个用于侧扫声纳墙壁检测的数据集，包含由轻量自主水下航行器在港口环境中采集的864张增强图像（原始216张），标注了墙壁和非墙壁两类对象，共2,616个样本，遵循COCO格式并划分为训练、验证和测试集。数据集还附带一段侧扫声纳视频及6,243帧提取图像，旨在支持计算机视觉算法如YOLOX-ViT的研究，适用于海洋机器人可靠性工程领域。