Abstract
Recent years have witnessed great advances in object segmentation research. In addition to generic objects, aquatic animals have attracted research attention. Deep learning-based methods are widely used for aquatic animal segmentation and have achieved promising performance. However, there is a lack of challenging datasets for benchmarking. Therefore, we have created a new dataset dubbed "Aquatic Animal Species." Furthermore, we devised a novel multimodal-based scene-aware segmentation framework that leverages the advantages of multiple view segmentation models to segment images of aquatic animals effectively. To improve training performance, we developed a guided mixup augmentation method. Extensive experiments comparing the performance of the proposed framework with state-of-the-art instance segmentation methods demonstrated that our method is effective and that it significantly outperforms existing methods.
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URL
https://arxiv.org/abs/2112.06193
