SWDD: Sonar Wall Detection Dataset

This repository contains three side scan sonar datasets:

• SWDD: Sonar Wall Detection Dataset, which is part of the paper Knowledge Distillation in YOLOX-ViT for Side Scan Sonar Object Detection. 
• SWDD-Validation is an extension version and part of the paper ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection.
• SWDD-Adversarial is another extended version and part of the paper ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection.

SWDD Dataset

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-Validation, 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.

⚠️The two following datasets (SWDD-Validation and SWDD-Adversarial) are parts of the paper ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection, submitted to the  2025 IEEE Symposium on Maritime Informatics & Robotics (MARIS 2025). The paper is still under review. ⚠️

SWDD-Validation

The SWDD dataset has been recorded using the LAUV with the Klein 3500 side scan sonar deployed in the Porto de Leixões harbor following the harbor's walls. The SWDD-Validation comports three field-collected detected: SWDD-Clean, SWDD-Surface, and SWDD-Noisy. The SWDD-Clean dataset, which includes data from the same mission as the original SWDD dataset; the SWDD-Surface dataset, captured while the LAUV was on the surface during windy weather, featuring a non-straight wall and wave-induced variations; and the SWDD-Noisy dataset, collected under stormy conditions, where the SSS transducer intermittently exited the water, resulting in data loss represented by black lines in the images.

The SWDD-Validation dataset provides three datasets collected under different weather and sonar setups aiming to study object detection models' robustness variation under different sonar and noise conditions. The metadata of the SWDD-Validation dataset is depicted in the following table.