Ornamental fish dataset from an underwater environment

In underwater environments, there are more than 33,000 species of fish, which are identified by different visual characteristics, such as the shape, color, and shape of the head. These characteristics are difficult to identify for ordinary people, therefore scientists and aquaculturists are using photographic and video cameras as tools to quantify the species and identify the state and shape. Providing these tools with computer vision algorithms and deep learning techniques, since recorded images of fish can be time-consuming and expensive to process and analyze manually, it is an interesting problem for researchers. However, the use of these techniques depends on the visual characteristics extracted by the set of images with which it has been trained. This article presents an image data set of 3 different fish species, Goldenfish (Carassius auratus), Molly (Poecilia sphenops), and Zebra (Danio rerio), obtaining a different number of images per species. The data were obtained by means of a camera in a natural environment where the species, housed in the environment protected by the aquaculturist.

搜集汇总

背景与挑战

背景概述

该数据集是一个水下环境观赏鱼图像数据集，包含Goldenfish、Molly和Zebra三种鱼类的图像，每物种图像数量不等，旨在支持计算机视觉和深度学习研究，以自动化识别鱼类物种和状态。数据通过自然环境下相机拍摄获取，突出了其真实应用场景，解决了人工分析水下鱼类图像耗时昂贵的问题。