Challenges / Marine Vision Restoration Challenge
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The MarineVision Restoration Challenge (MVRC), the first of its kind as a part of MaCVi Workshop @ WACV 2025, aims to restore underwater images for marine species detection and addresses key issues of marine exploration and conservation efforts. The underwater environment presents unique difficulties for image capture, including poor visibility, color distortion, and light scattering, which significantly hinder accurate marine species detection. This challenge aims to bring researchers from around the world to develop innovative solutions for improving the clarity of underwater images. By restoring the image quality and enabling precise marine species detection, participants will contribute to the broader goal of supporting marine biodiversity studies and ecosystem monitoring. The challenge consists of two phases. In the first phase, we focus on restoring degraded underwater images, and in the second phase, we culminate with the detection and classification of marine species as shown in overview figure.
We provide the dataset comprising of multiple degradation patterns resembling tints and properties of water from various geographic locations like seas, oceans, ponds, lakes etc. The dataset provided comprises of all required annotations and the metadata for training restoration and detection models.
Given degraded underwater images/videos/frames, the participant needs to develop a method for restoring these images/videos/frames, and subsequently use an object detection model to detect and classify the marine species in the images/videos/frames.
We provide Realistic Synthetically Generated Underwater Paired Dataset (RSUIGM). The dataset comprises of 6000 images which has characteristics of both oceanic beds, and coastal bed region along with varying depths. This data simulates water properties from various geographic locations. Each pair of data also has corresponding annotation for training object detection models. Each scene in the dataset comprises of 1 ground-truth image and 200 input images (1:200 ratio between ground-truth and input images). The dataset hierarchy is as follows: |——Root Dir |              | - Train |                       | - Ground-truth Images |                       | - Input Images |                       | - Labels for OD |              | - Validation |                       | - Input Images |              | - Test |                       | - Input Images
For evaluating the submissions, we use a combination of metrics tailored to both image restoration and marine species detection/classification tasks. For underwater image restoration, the results will be primarily assessed using UIQM, UCIQE, and CCF metrics, which focus on underwater image quality. In the detection and classification phase, performance will be evaluated using Mean IoU (mIoU), F1 Score, and mean Average Precision (mAP), which provides a comprehensive assessment for detection and classification models.
To participate in the challenge follow these steps: