ArticleACS central science2020
Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set.
Article in ACS central science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Fast Image Segmentation Toward Automation of 3D Ice Printing.Chemical & biomedical imaging · 2026Article
- MODNet: A Monocular Object-Based Depth Estimation Network for AI Robotic Chemists.Precision chemistry · 2026Article
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Localization, inspection, and reasoning (LIRA) module for autonomous workflows in self-driving laboratories.Communications chemistry · 2025Article
- A Visual Dataset for Anomaly Detection in Self-Driving Laboratories.Scientific data · 2025Article
- Steering towards safe self-driving laboratories.Nature reviews. Chemistry · 2025Review
- AI Approaches to Homogeneous Catalysis with Transition Metal Complexes.ACS catalysis · 2025Review
- A Software-Defined Sensor System Using Semantic Segmentation for Monitoring Remaining Intravenous Fluids.Sensors (Basel, Switzerland) · 2025Article
- Intelligent Systems for Inorganic Nanomaterial Synthesis.Nanomaterials (Basel, Switzerland) · 2025Review
- Parameter efficient multi-model vision assistant for polymer solvation behaviour inference.npj computational materials · 2025Article
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Comprehensive image dataset for enhancing object detection in chemical experiments.Data in brief · 2024Article
- A focus on computer vision for non-contact monitoring of catalyst degradation and product formation kinetics.Chemical science · 2023Article
- Go with the flow: deep learning methods for autonomous viscosity estimations.Digital discovery · 2023Article
- Machine Learning in Unmanned Systems for Chemical Synthesis.Molecules (Basel, Switzerland) · 2023Review
- Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab.Accounts of chemical research · 2022Article
- Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials.Chemical science · 2021Article
- Article
- Automation of Synthesis in Medicinal Chemistry: Progress and Challenges.ACS medicinal chemistry letters · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work presents a machine learning approach for the computer vision-based recognition of materials inside vessels in the chemistry lab and other settings. In addition, we release a data set associated with the training of the model for further model development. The task to learn is finding the region, boundaries, and category for each material phase and vessel in an image. Handling materials inside mostly transparent containers is the main activity performed by human and robotic chemists in the laboratory. Visual recognition of vessels and their contents is essential for performing this task. Modern machine-vision methods learn recognition tasks by using data sets containing a large number of annotated images. This work presents the Vector-LabPics data set, which consists of 2187 images of materials within mostly transparent vessels in a chemistry lab and other general settings. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder, ...). The fill level, labels, corks, and parts of the vessel are also annotated. Several convolutional nets for semantic and instance segmentation were trained on this data set. The trained neural networks achieved good accuracy in detecting and segmenting vessels and material phases, and in classifying liquids and solids, but relatively low accuracy in segmenting multiphase systems such as phase-separating liquids.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.