全球尺度下的海洋鱼类图像智能分类研究进展

A Review of the Artificial Intelligence-based Image Classification of Fishes in the Global Oceans

在全球尺度上了解鱼类物种组成、丰度及时空分布等,将有助于其生物多样保护。水下图像采集是获取鱼类物种多样性数据的主要调查手段之一,但图像信息分析工作耗时耗力。2015年以来,海洋鱼类图像数据集更新和深度学习模型算法优化等方面取得了一系列进展,但细粒度分类表现仍显不足,研究成果的生产实践应用相对薄弱。因此,该文首先分析海洋相关行业对鱼类自动化图像分类的需求,然后综合介绍鱼类图像数据集和深度学习算法应用,并分析了所面临的小样本下的细粒度分析等主要挑战及相应解决方法。最后探讨了基于深度学习的海洋鱼类图像自动化分类对相关图像信息处理研究及应用平台对未来在生态环境监测等海洋相关产业领域的重要性及其前景。该文旨在为快速了解基于深度学习的海洋鱼类图像自动化分类的研究背景、进展和未来方向的工作者提供相关信息。

Understanding the species composition,abundance and temporal and spatial distribution of fish on a global scale will help their biodiversity conservation.Underwater image acquisition is one of the main means to survey fish species diversity,but image data analysis is time-consuming and labor-intensive.Since 2015,a series of progress has been made in updating the datasets of marine fish images and optimizing the algorithm of deep learning models,but the performance of fine-grained classification is still insufficient,and the production practice application of research results is relatively weak.Therefore,the need for automated fish image classification in marine investigations is firstly studied.Then a comprehensive introduction to fish image datasets and deep learning algorithm applications is provided,and the main challenges and the corresponding solutions are analyzed.Finally,the importance of automated classification of marine fish images for related image information processing research is discussed,and its prospects in the field of marine monitoring are summarized.