DeepFish

Image bank of fish trays collected in the Campello fish market by artisanal fishing belonging to the DeepFish project. JSON files are prepared to be used with the program "Django Labeller", but it can be converted to any format, f.e. "COCO". They are composed by an object containing the name of the image and the labels appearing in it. Inside each label we will have: Type of label. It can be a size (total, diameter of the eye...), tray or fish specie. Class of the label. It means the concrete specie, measurement or tray depending on the type of label. Semantic segmentation represented by one or multiple regions in case of occlusions. Represented by an array of coordinates in the image (x and y) Object_id: Identifier of the label, unique in the entire dataset. Father_object_id: In case this is not the main object (The label with the segmentation of the specie). It will point to the id of that main specie which it belongs. It means, if this is the total size, it will point to the fish sized like that.

本数据集为DeepFish项目依托手工捕捞作业，于坎佩洛（Campello）鱼市采集的渔获托盘图像库。配套JSON文件可配合工具"Django Labeller"使用，也可转换为COCO等任意格式。数据集的JSON数据以对象为基本单元，每个对象包含对应图像的名称及图内出现的标注标签信息。每个标签项下包含以下属性：其一为标签类型，可分为尺寸类（总体尺寸、眼部直径等）、托盘类及鱼类物种类三类；其二为标签类别，根据标签类型的不同，分别对应具体鱼类物种、测量参数或托盘信息；其三为语义分割标注，针对存在遮挡的场景，以单个或多个区域进行标注，通过图像坐标系（x、y轴）下的坐标数组实现；其四为Object_id，即标签唯一标识符，在全数据集范围内保持唯一；其五为Father_object_id，当该标签并非主目标（主目标指带有物种分割标注的标签）时，该字段将指向其所属主物种标签的ID。例如，若当前为总体尺寸标签，则将指向对应鱼类的尺寸标注ID。

搜集汇总

背景与挑战

背景概述

DeepFish是一个包含手工捕捞鱼盘图像的标注数据集，支持多种标注格式转换，适用于深度学习和语义分割任务。数据集详细标注了鱼类种类、尺寸和位置信息，适合用于机器学习和计算机视觉研究。