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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.

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:71221
Date26 June 2020
CreatorsGeiger, Chris, Maier, Niklas, Kalinke, Florian, Geimer, Marcus
ContributorsDresdner Verein zur Förderung der Fluidtechnik e. V. Dresden
PublisherTechnische Universität Dresden
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text
Rightsinfo:eu-repo/semantics/openAccess
Relation10.25368/2020.8, urn:nbn:de:bsz:14-qucosa2-709188, qucosa:70918

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