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Real-time 3D Semantic Segmentation of Timber Loads with Convolutional Neural Networks
Linköping University, Department of Electrical Engineering, Computer Vision.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Volume measurements of timber loads is done in conjunction with timber trade. When dealing with goods of major economic values such as these, it is important to achieve an impartial and fair assessment when determining price-based volumes.

With the help of Saab’s missile targeting technology, CIND AB develops products for digital volume measurement of timber loads. Currently there is a system in operation that automatically reconstructs timber trucks in motion to create measurable images of them. Future iterations of the system is expected to fully automate the scaling by generating a volumetric representation of the timber and calculate its external gross volume. The first challenge towards this development is to separate the timber load from the truck.

This thesis aims to evaluate and implement appropriate method for semantic pixel-wise segmentation of timber loads in real time. Image segmentation is a classic but difficult problem in computer vision. To achieve greater robustness, it is therefore important to carefully study and make use of the conditions given by the existing system. Variations in timber type, truck type and packing together create unique combinations that the system must be able to handle. The system must work around the clock in different weather conditions while maintaining high precision and performance.

Place, publisher, year, edition, pages
2018. , p. 49
Keywords [en]
Semantic Segmentation, Pixel-wise classification, Convolutional Neural Networks, Fully Convolutional Networks, Patch-based training.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-148862ISRN: LiTH-ISY-EX--18/5131--SEOAI: oai:DiVA.org:liu-148862DiVA, id: diva2:1222024
External cooperation
Saab Dynamics AB
Subject / course
Computer Vision Laboratory
Supervisors
Examiners
Available from: 2018-06-21 Created: 2018-06-20 Last updated: 2018-06-25Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Output format
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