Improved deep learning framework for fish segmentation in underwater videos

Deep learning networks have become increasingly popular in recent years due to promising breakthroughs achieved in several areas. The importance of deep learning lies in the localisation and classification of an object based on frames. This study focuses on fish recognition methods in underwater videos and addresses the underlying challenges of these methods. It is important to develop effective methods to recognise fish and their movements using underwater videos. From a practical and scientific perspective, this is extremely useful to automatically recognise fish through their movement and to monitor and collect biomass in marine bodies. More importantly, it allows researchers to collect and analyse information related to the health and well-being of the Marine ecosystem. As most of the current methods work on static images, the issue arises when these methods are applied to images from videos. The existing multiple fish detection methods for underwater videos have a low detection rate due to the inherent underwater conditions such as the presence of coral reefs and other challenges which include the different sizes, shapes, colour, and speed of fish as well as marine behaviours such as the overlapping of fish. Therefore, the use of improved methods based on the latest deep learning algorithms has been proposed for multiple fish detection. This paper provides a novel framework for fish instance segmentation in underwater videos. The proposed model for improved recognition methods is composed of four main stages: 1) pre-processing method to reduce external factors in the videos for better detection and recognition of fish in underwater videos, 2) use of deep learning approach for enhanced detection of fish using RESENT, 3) enhanced detection of multiple fish based on the Region Proposal Network (RPN) architecture, and 4) use of a dynamic instance segmentation method. The results of this study indicate that the proposed framework has a better performance capability than other state-of-the-art models for multi-fish instance segmentation.