Underwater Image Restoration

We released a large-scale underwater image (LSUI) dataset including 5004 image pairs, which involve richer underwater scenes (lighting conditions, water types and target categories) and better visual quality reference images than the existing ones.

The dataset contains single-shot videos taken from moving cameras in underwater environments. The first shard of a new Marine Video Kit dataset is presented to serve for video retrieval and other computer vision challenges. In addition to basic meta-data statistics, we present several insights based on low-level features as well as semantic annotations of selected keyframes. 1379 videos with a length from 2 s to 4.95 min, with the mean and median duration of each video is 29.9 s, and 25.4 s, respectively. We capture data from 11 different regions and countries during the time from 2011 to 2022.

Description: <a href="https://gts.ai/dataset-download/underwater-object-detection-dataset/" target="_blank">👉 Download the dataset here</a> This dataset is designed for advanced underwater object detection and classification. It provides a comprehensive collection of images featuring underwater objects, each precisely annotated with bounding boxes. The dataset aims to support a wide range of research applications, from environmental monitoring to underwater robotics. Download Dataset Classes: Fish (individual and grouped) Crab Human Diver Trash (marine pollution) Jellyfish Coral Reef Sea Turtle Starfish Dataset Structure: Training Set (70%): A robust sample for building detection models. Validation Set (10%): Used to fine-tune model performance. Test Set (20%): A carefully selected set of images for evaluating model accuracy. Pre-processing Techniques: Auto-Orientation: Ensures all images are correctly aligned. Resizing: Images are scaled to 640×640 pixels for uniformity. Brightness Normalization: Corrects for underwater lighting conditions. Contrast Stretching: Enhances visibility for objects in murky or low-contrast scenes. New Annotation Techniques: Polygonal Segmentation: Introduces more precise segmentation for irregular shapes such as coral reefs. 3D Depth Mapping: For enhanced understanding of object placement in underwater space. Dataset Use Cases: Marine Ecology: Assessing species diversity and tracking the impact of environmental changes. Pollution Analysis: Detecting and classifying marine trash, aiding in cleanup efforts. Underwater Robotics: Training AUVs to recognize and navigate around complex underwater structures like coral reefs or large groups of fish. Conclusion: The expanded Underwater Object Detection provides a rich resource for researchers, environmentalists, and engineers working on underwater object detection and classification. Its enhanced classes, precise annotations, and preprocessing techniques make it a valuable asset for developing robust models in marine exploration and conservation. This dataset is sourced from Kaggle.