Computer-aided fish assessment in an underwater marine environment using parallel and progressive spatial information fusion

Abstract

Fish assessment and monitoring are important for the development of a modern aquatic ecosystem. Fish are a vital part of the marine and freshwater environments. Morphological and computational details of fish, such as size, shape, and position, are important in fish observation and fisheries. Typically, manual, or low-efficient techniques are used to acquire fish details. However, existing typical methods are usually time-consuming, less accurate, and resource-intensive. Computer-aided methods are crucial for intelligent and automatic fish assessment. Two novel networks, namely parallel feature fusion-based segmentation network (PFFS-Net) and progressive information fusion-based segmentation network (PIFS-Net), were developed for pixel-wise fish segmentation. PFFS-Net is a base network that uses parallel feature fusion to achieve a better segmentation performance. PIFS-Net is the final model of this work and uses a progressive spatial feature fusion (SFF) mechanism to enhance segmentation accuracy. PIFS-Net also employs rapid feature reduction and pre-prediction low-level information fusion blocks to further boost performance.The proposed models were evaluated using the following three publicly available databases: semantic segmentation of underwater imagery (SUIM), DeepFish, and Large-scale fish. The proposed networks outperformed the state-of-the-art methods in challenging underwater conditions with superior computational efficiency. PIFS-Net needs only 2.02 million trainable parameters for its complete training. Automatic and accurate fish segmentation can be a major step towards an intelligent aquatic ecosystem. The codes of our algorithms and trained models are available on Github.

Keywords

• Deep learning
• Underwater computer vision
• Artificial intelligence in marine
• Fish segmentation
• PFFS-Net and PIFS-Net