Bridging the Sim-to-Real GAP for Underwater Image Segmentation

Labeling images for every new task or data pattern a model needs to learn is a significant time bottleneck in real-world applications. Moreover, acquiring the necessary data for training the models can be challenging. Ideally, one would train the models with simulated images and adapt them for the desired real tasks using the least possible amount of data. Active learning can be used to solve this problem with minimal effort. In this work, we train SegFormer for pipeline segmentation with synthetic images from an underwater simulated environment and fine-tune the model with real underwater pipeline images recorded in a marina. The evaluation shows that selecting real data with active learning for fine-tuning the model gives better results than randomly selecting the images. As part of the work, we release the dataset recorded in the marina, MarinaPipe, which will be publicly available.