Marine Animal Segmentation

In recent years, marine animal study has gained increasing research attention, which raises significant demands for fine-grained marine animal segmentation (MAS) techniques. In addition, deep learning has been widely adopted for object segmentation and has achieved promising performance. However, deep-based MAS is still lack of investigation due to the shortage of a large-scale MAS dataset. To tackle this issue, we construct the first large-scale MAS dataset, called MAS3K, which consists of 3,103 images from different types, including camouflaged marine animal images, common marine animal images, and underwater images without marine animals. Furthermore, we consider different underwater conditions, such as low illumination, turbid water quality, photographic distortion, etc. Each image from MAS3K dataset has rich annotations, including an object-level mask, a category name, attributes, and a camouflage method (if applicable). Furthermore, we propose a novel MAS network, called Enhanced Cascade Decoder Network (ECD-Net), which consists of multiple Interactive Feature Enhancement Modules (IFEMs) and Cascade Decoder Modules (CDMs). In ECD-Net, the IFEMs are first utilized to extract rich multi-scale features. The resulting features are then fed to the CDMs for accurately segmenting marine animals from complex underwater environments. We perform extensive experiments to compare ECD-Net with 10 cutting-edge object segmentation models. The results demonstrate that ECD-Net is an effective MAS model and outperforms the cutting-edge models, both qualitatively and quantitatively.