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.

近年来，由于在多个领域取得了令人鼓舞的突破，深度学习网络变得越来越流行。深度学习的重要性在于基于框架的对象的定位和分类。这项研究的重点是水下视频中的鱼类识别方法，并解决了这些方法的潜在挑战。开发使用水下视频识别鱼类及其活动的有效方法非常重要。从实践和科学的角度来看，这对于通过其运动自动识别鱼并监视和收集海洋生物量非常有用。更重要的是，它使研究人员可以收集和分析与海洋生态系统的健康和福祉相关的信息。由于目前大多数方法都适用于静态图片，当这些方法应用于视频图像时，就会出现问题。由于存在固有的水下条件，例如珊瑚礁的存在以及其他挑战，包括不同尺寸，形状，颜色和速度的鱼类以及诸如海洋行为等现有挑战，现有的水下视频鱼类检测方法的检测率很低。如鱼的重叠。因此，已提出使用基于最新深度学习算法的改进方法来进行多条鱼检测。本文为水下视频中的鱼类实例分割提供了一个新颖的框架。所提出的改进识别方法模型包括四个主要阶段：1）预处理方法，以减少视频中的外部因素，以便更好地检测和识别水下视频中的鱼，2）使用深度学习方法使用RESENT增强对鱼类的检测，3）基于区域提议网络（RPN）架构增强对多种鱼类的检测，以及4）使用动态实例分割方法。这项研究的结果表明，所提出的框架比其他用于多鱼实例分割的最新模型具有更好的性能。