ArticleScientific reports2022
Improving remote material classification ability with thermal imagery.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Multimodal Material Classification Using Visual Attention.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Material recognition using optical sensors is a key enabler technology in the field of automation. Nowadays, in the age of deep learning, the challenge shifted from (manual) feature engineering to collecting big data. State of the art recognition approaches are based on deep neural networks employing huge databases. But still, it is difficult to transfer these latest recognition results into the wild-various lighting conditions, a changing image quality, or different and new material classes are challenging complications. Evaluating a larger electromagnetic spectrum is one way to master these challenges. In this study, the infrared (IR) emissivity as a material specific property is investigated regarding its suitability for increasing the material classification reliability. Predictions of a deep learning model are combined with engineered features from IR data. This approach increases the overall accuracy and helps to differentiate between materials that visually appear similar. The solution is verified using real data from the field of automatized disinfection processes.
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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.