Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines

Gespeichert in:

Personen und Körperschaften:	Geiger, Chris, Maier, Niklas, Kalinke, Florian, Geimer, Marcus
Titel:	Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines
Format:	E-Book
Sprache:	Englisch
veröffentlicht:	Dresden Technische Universität Dresden Online-Ausg.. 2020
Schlagwörter:	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
Quelle:	Qucosa

Details
Zusammenfassung:	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.