LabLiquidVision

The LabLiquidVolume dataset includes 5,451 images of liquids in laboratory containers. The images were taken using an Intel RealSense D415 camera in different environments in the automation laboratory and various research laboratories at the Novo Nordisk R&eD site in Måløv, Denmark. The ground truth of the liquid volume was measured using a Mettler Toledo XSR2002S balance with an accuracy of ± 0.5 mL. Twelve of the most common research laboratory containers, including the consumables used in cell culture processes, were selected. Various liquid volumes (3 - 600 mL), distances to the containers (50 - 600 mm), backgrounds, and camera angles were used. However, all images were taken from above so that the surface of the liquid is visible. In consultation with laboratory scientists, three different liquid colors were chosen: red, green, and blue. Besides transparent liquids, these are the most frequent colors of liquids in laboratory experiments. For the final dataset, the content is extended by the output of the segmentation and depth estimation model using the RGB images as input. This includes the segmented liquid and vessel depth maps, the liquid and vessel segmentation masks (each as .png and .npy files), and the unsegmented depth maps of liquid and vessel. The vessels in the images in the dataset have an average maximum volume of 178 mL and an average fill proportion of 48%. 31% percent of the samples contain green, 57% red, and 12% blue liquid.Using this dataset, we propose a vision-based liquid volume estimation using a novel two-step Convolutional Neural Network (CNN) architecture. In the first step, a single RGB input image is processed by the first CNN to predict the segmentation and depth of the transparent container and the containing liquid. For the training of the first step, we benefit from existing datasets targeting transparent containers such as TransProteus and Vector-LabPics. These intermediate predictions are further processed by a second CNN to give an estimate of the liquid volume in the container. For the training of the second network, the proposed new dataset LabLiquidVolume is used.

LabLiquidVolume数据集包含5451张实验室容器内液体的图像。所有图像均由英特尔RealSense D415相机拍摄，拍摄场景涵盖丹麦马洛夫市诺和诺德研发（R&D）基地内的自动化实验室与各类研究实验室。液体体积的真值（ground truth）采用精度为±0.5 mL的梅特勒托利多XSR2002S天平进行测量。本次数据集选取了12种最常用的实验室研究用容器，包括细胞培养流程中使用的耗材。实验覆盖了不同的液体体积（3~600 mL）、容器拍摄距离（50~600 mm）、背景环境与拍摄角度，但所有图像均采用俯视视角拍摄，以确保液体表面清晰可见。经与实验室科研人员协商，本次数据集选取了红、绿、蓝三种常见液体色彩，除透明液体外，这三类也是实验室实验中最常见的液体色调。最终数据集还补充了以RGB图像为输入的分割与深度估计模型的输出结果，具体包括：分割后的液体与容器深度图、液体和容器的分割掩码（均保存为.png与.npy格式文件），以及未经过分割处理的液体与容器深度图。数据集中图像内的容器平均最大容积为178 mL，平均装填比例为48%。样本中31%为绿色液体、57%为红色液体、12%为蓝色液体。基于本数据集，本文提出了一种基于视觉的液体体积估计方法，采用全新的两步卷积神经网络（Convolutional Neural Network, CNN）架构。第一步中，首个卷积神经网络将单张RGB输入图像作为输入，预测透明容器及其内部液体的分割结果与深度信息。第一步模型的训练可依托现有的透明容器相关数据集，例如TransProteus与Vector-LabPics。上述中间预测结果将被送入第二个卷积神经网络进行进一步处理，最终输出容器内液体体积的估计值。第二步网络的训练则采用本文提出的LabLiquidVolume新数据集。

搜集汇总

背景与挑战

背景概述

LabLiquidVision是一个包含5,451张实验室容器液体图像的数据集，涵盖不同颜色、体积和拍摄条件，并附带分割和深度估计结果。该数据集专为训练两阶段CNN架构的液体体积估计模型而设计。