Comparative Analysis of Deep Neural Networks for the Detection and Decoding of Data Matrix Landmarks in Cluttered Indoor Environments
2021 (English)In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 103, no 1, article id 13Article in journal (Refereed) Published
Abstract [en]
Data Matrix patterns imprinted as passive visual landmarks have shown to be a valid solution for the self-localization of Automated Guided Vehicles (AGVs) in shop floors. However, existing Data Matrix decoding applications take a long time to detect and segment the markers in the input image. Therefore, this paper proposes a pipeline where the detector is based on a real-time Deep Learning network and the decoder is a conventional method, i.e. the implementation in libdmtx. To do so, several types of Deep Neural Networks (DNNs) for object detection were studied, trained, compared, and assessed. The architectures range from region proposals (Faster R-CNN) to single-shot methods (SSD and YOLO). This study focused on performance and processing time to select the best Deep Learning (DL) model to carry out the detection of the visual markers. Additionally, a specific data set was created to evaluate those networks. This test set includes demanding situations, such as high illumination gradients in the same scene and Data Matrix markers positioned in skewed planes. The proposed approach outperformed the best known and most used Data Matrix decoder available in libraries like libdmtx.
Place, publisher, year, edition, pages
Springer, 2021. Vol. 103, no 1, article id 13
Keywords [en]
Deep learning, Data matrix, Detection, Decoding, Localization
National Category
Computer graphics and computer vision Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-93889DOI: 10.1007/s10846-021-01442-xISI: 000684216000002Scopus ID: 2-s2.0-85112339177OAI: oai:DiVA.org:oru-93889DiVA, id: diva2:1587552
Funder
Knut and Alice Wallenberg Foundation
Note
Funding Agencies:
Örebro University
Spanish Ministerio de Ciencia, Innovacion y Universidades RTI2018-095599-A-C22
Project SeaAI-FA 02 2017 011
Wallenberg AI, Autonomous Systems and Software Program (WASP) Project PRODUTECH II SIF-POCI-01-0247-FEDER-024541
2021-08-252021-08-252025-02-01Bibliographically approved