Optical Load Detection: Load Weighing for Construction Machines using Stereo Vision and Convolutional Neural Networks

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dc.contributor.authorStråhle, Daniel
dc.contributor.authorWingård Olsson, Kevin
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mekanik och maritima vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mechanics and Maritime Sciencesen
dc.contributor.examinerForsberg, Peter
dc.contributor.supervisorAndreasson, Mathias
dc.date.accessioned2023-11-17T12:26:21Z
dc.date.available2023-11-17T12:26:21Z
dc.date.issued2022
dc.date.submitted2023
dc.description.abstractAccurate excavation monitoring is important for the handling of materials within the construction industry. Modern construction machines provide built-in systems for weighing handled goods. In this thesis, an alternative optical weighing system is developed and implemented for an excavator and a wheel loader. The optical system detects and provides the volume and weight of the handled material through fill-factor estimation. The methodology is based on depth data and images captured by a stereo camera, mounted on the machines. By using a region-based convolutional neural network (CNN), localization of material and fill-factor estimation are managed jointly. Material classification is also proved to be possible using gathered images and a simple CNN. By combining the fill-factor and information about the material, weight is obtained. Evaluations reveal that the system measures fill-factor to mean absolute percentage errors (MAPE), relative to the maximum capacity of the excavator and the wheel loader, of 3.3 % and 3.0 % respectively.
dc.identifier.coursecodeMMSX30
dc.identifier.urihttp://hdl.handle.net/20.500.12380/307383
dc.language.isoeng
dc.relation.ispartofseries2022:21
dc.setspec.uppsokTechnology
dc.subjectExcavation Monitoring
dc.subjectCNN
dc.subjectFaster R-CNN
dc.subjectRPN
dc.subjectRange Sensor
dc.subjectStereo Camera
dc.subjectComputer Vision
dc.subjectMaterial Classification
dc.titleOptical Load Detection: Load Weighing for Construction Machines using Stereo Vision and Convolutional Neural Networks
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeSystems, control and mechatronics (MPSYS), MSc
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