SOLR
| _version_ |
1797727634091671552 |
| author |
Geiger, Chris |
| author2 |
Maier, Niklas, Kalinke, Florian, Geimer, Marcus |
| author2_role |
, , |
| author2_variant |
n m nm, f k fk, m g mg |
| author_facet |
Geiger, Chris, Maier, Niklas, Kalinke, Florian, Geimer, Marcus |
| author_role |
|
| author_sort |
Geiger, Chris |
| author_variant |
c g cg |
| building |
Library A |
| collection |
sid-22-col-qucosa |
| contents |
The correct classification of a logs assortment is crucial for the economic output within a fully mechanized timber harvest. This task is especially for unexperienced but also for professional machine operators mentally demanding. This paper presents a method towards an assistance system for machine operators for an automated log quality and assortment estimation. Therefore, machine vision methods for object detection are combined with machine learning approaches for estimating the logs weight based on a Convolutional Neural Network (CNN). Based on the dimensions oft he object ´log, a first categorisation into a specific assortment is done. By comparing the theoretical weight of a healthy log of such dimensions to the real weight estimated by the CNN-based crane scale, quality reducing properties such as beetle infestation or red rod can be detected. In such cases, the assistance system displays a visual warning to the operator to check the loaded log. |
| dewey-full |
620 |
| dewey-hundreds |
600 - Technology (Applied sciences) |
| dewey-ones |
620 - Engineering and allied operations |
| dewey-raw |
620 |
| dewey-search |
620 |
| dewey-sort |
3620 |
| dewey-tens |
620 - Engineering and allied operations |
| facet_avail |
Online, Free |
| finc_class_facet |
Technik |
| fincclass_txtF_mv |
engineering-process, technology |
| format |
eBook |
| format_access_txtF_mv |
Book, E-Book |
| format_de105 |
Ebook |
| format_de14 |
Book, E-Book |
| format_de15 |
Book, E-Book |
| format_del152 |
Buch |
| format_detail_txtF_mv |
text-online-monograph-independent-conference |
| format_dezi4 |
e-Book |
| format_finc |
Book, E-Book |
| format_legacy |
ElectronicBook |
| format_legacy_nrw |
Book, E-Book |
| format_nrw |
Book, E-Book |
| format_strict_txtF_mv |
E-Book |
| genre |
Konferenzschrift |
| genre_facet |
Konferenzschrift |
| geogr_code |
not assigned |
| geogr_code_person |
not assigned |
| id |
22-14-qucosa2-712216 |
| illustrated |
Not Illustrated |
| imprint |
Dresden, Technische Universität Dresden |
| imprint_str_mv |
Online-Ausg.: 2020 |
| institution |
DE-105, DE-Gla1, DE-Brt1, DE-D161, DE-540, DE-Pl11, DE-Rs1, DE-Bn3, DE-Zi4, DE-Zwi2, DE-D117, DE-Mh31, DE-D275, DE-Ch1, DE-15, DE-D13, DE-L242, DE-L229, DE-L328 |
| is_hierarchy_id |
|
| is_hierarchy_title |
|
| language |
English |
| last_indexed |
2024-04-30T03:11:12.038Z |
| match_str |
geiger2020assistancesystemforanautomatedlogqualityandassortmentestimationbasedondatadrivenapproachesusinghydraulicsignalsofforestrymachines |
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Qucosa |
| publishDate |
|
| publishDateSort |
2020 |
| publishPlace |
Dresden |
| publisher |
Technische Universität Dresden |
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marcfinc |
| record_id |
14-qucosa2-712216 |
| recordtype |
marcfinc |
| rvk_facet |
Zq 5460 |
| source_id |
22 |
| spelling |
Geiger, Chris, Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines, Dresden Technische Universität Dresden, txt, nc, Online-Ausg. 2020 Online-Ressource (Text) Technische Universität Dresden, The correct classification of a logs assortment is crucial for the economic output within a fully mechanized timber harvest. This task is especially for unexperienced but also for professional machine operators mentally demanding. This paper presents a method towards an assistance system for machine operators for an automated log quality and assortment estimation. Therefore, machine vision methods for object detection are combined with machine learning approaches for estimating the logs weight based on a Convolutional Neural Network (CNN). Based on the dimensions oft he object ´log, a first categorisation into a specific assortment is done. By comparing the theoretical weight of a healthy log of such dimensions to the real weight estimated by the CNN-based crane scale, quality reducing properties such as beetle infestation or red rod can be detected. In such cases, the assistance system displays a visual warning to the operator to check the loaded log., 12Th International Fluid Power Conference, Assistance System, Log Assortment, Crane Scale, Machine Learning, Machine Vision, Forwarder, Convolutional Neural Network, 12. Ifk, Assistenzsystem, Protokollsortiment, Kranwaage, Maschinelles Lernen, Bildverarbeitung, Weiterleitung, Faltendes Neuronales Netz, Konferenzschrift, Maier, Niklas, Kalinke, Florian, Geimer, Marcus, text/html https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-712216 Online-Zugriff |
| spellingShingle |
Geiger, Chris, Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines, The correct classification of a logs assortment is crucial for the economic output within a fully mechanized timber harvest. This task is especially for unexperienced but also for professional machine operators mentally demanding. This paper presents a method towards an assistance system for machine operators for an automated log quality and assortment estimation. Therefore, machine vision methods for object detection are combined with machine learning approaches for estimating the logs weight based on a Convolutional Neural Network (CNN). Based on the dimensions oft he object ´log, a first categorisation into a specific assortment is done. By comparing the theoretical weight of a healthy log of such dimensions to the real weight estimated by the CNN-based crane scale, quality reducing properties such as beetle infestation or red rod can be detected. In such cases, the assistance system displays a visual warning to the operator to check the loaded log., 12Th International Fluid Power Conference, Assistance System, Log Assortment, Crane Scale, Machine Learning, Machine Vision, Forwarder, Convolutional Neural Network, 12. Ifk, Assistenzsystem, Protokollsortiment, Kranwaage, Maschinelles Lernen, Bildverarbeitung, Weiterleitung, Faltendes Neuronales Netz, Konferenzschrift |
| title |
Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines |
| title_auth |
Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines |
| title_full |
Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines |
| title_fullStr |
Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines |
| title_full_unstemmed |
Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines |
| title_short |
Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines |
| title_sort |
assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines |
| title_unstemmed |
Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines |
| topic |
12Th International Fluid Power Conference, Assistance System, Log Assortment, Crane Scale, Machine Learning, Machine Vision, Forwarder, Convolutional Neural Network, 12. Ifk, Assistenzsystem, Protokollsortiment, Kranwaage, Maschinelles Lernen, Bildverarbeitung, Weiterleitung, Faltendes Neuronales Netz, Konferenzschrift |
| topic_facet |
12Th International Fluid Power Conference, Assistance System, Log Assortment, Crane Scale, Machine Learning, Machine Vision, Forwarder, Convolutional Neural Network, 12. Ifk, Assistenzsystem, Protokollsortiment, Kranwaage, Maschinelles Lernen, Bildverarbeitung, Weiterleitung, Faltendes Neuronales Netz, Konferenzschrift |
| url |
https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-712216 |
| urn |
urn:nbn:de:bsz:14-qucosa2-712216 |
| work_keys_str_mv |
AT geigerchris assistancesystemforanautomatedlogqualityandassortmentestimationbasedondatadrivenapproachesusinghydraulicsignalsofforestrymachines, AT maierniklas assistancesystemforanautomatedlogqualityandassortmentestimationbasedondatadrivenapproachesusinghydraulicsignalsofforestrymachines, AT kalinkeflorian assistancesystemforanautomatedlogqualityandassortmentestimationbasedondatadrivenapproachesusinghydraulicsignalsofforestrymachines, AT geimermarcus assistancesystemforanautomatedlogqualityandassortmentestimationbasedondatadrivenapproachesusinghydraulicsignalsofforestrymachines |
