A synthetic Ordinary Differential Equation (ODE)-driven dataset for the detection of underwater moving objects

This dataset is developed for benchmarking domain-informed neural networks in the task of underwater moving object detection. The data samples are generated using the Unity Game Engine simulator. <br>The dataset consists of 1000 independent simulation runs, each creating a video segment. Each video is provided as a sequence of image frames, accompanied by time-synchronized ground-truth data describing the movement of the target object. More specifically, the dataset provides the object’s position, velocity, and acceleration over time, as well as frame timestamps. <br>YOLO bounding-box annotations are provided per frame and stored in a single JSON file for each video sequence. The images are arranged in folders corresponding to individual video sequences. All images are resized to a resolution of 1920×1080 pixels.<br>The dataset is intended for research and evaluation of computer vision methods that combine visual information with physics-based knowledge in underwater scenarios.