Efficient and consumer-centered item detection and classification with a multicamera network at high ranges

  • In the EU project SHAREWORK, methods are developed that allow humans and robots to collaborate in an industrial environment. One of the major contributions is a framework for task planning coupled with automated item detection and localization. In this work, we present the methods used for detecting and classifying items on the shop floor. Important in the context of SHAREWORK is the user-friendliness of the methodology. Thus, we renounce heavy-learning-based methods in favor of unsupervised segmentation coupled with lenient machine learning methods for classification. Our algorithm is a combination of established methods adjusted for fast and reliable item detection at high ranges of up to eight meters. In this work, we present the full pipeline from calibration, over segmentation to item classification in the industrial context. The pipeline is validated on a shop floor of 40 sqm and with up to nine different items and assemblies, reaching a mean accuracy of 84% at 0.85 Hz.

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Metadaten
Author:Nils MandischerORCiDGND, Tobias Huhn, Mathias Hüsing, Burkhard Corves
URN:urn:nbn:de:bvb:384-opus4-1178801
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/117880
ISSN:1424-8220OPAC
Parent Title (English):Sensors
Publisher:MDPI AG
Type:Article
Language:English
Year of first Publication:2021
Publishing Institution:Universität Augsburg
Release Date:2025/01/07
Volume:21
Issue:14
First Page:4818
DOI:https://doi.org/10.3390/s21144818
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Ingenieurinformatik mit Schwerpunkt Mechatronik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 60 Technik / 600 Technik, Technologie
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)