Optical Load Detection: Load Weighing for Construction Machines using Stereo Vision and Convolutional Neural Networks
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Systems, control and mechatronics (MPSYS), MSc
Publicerad
2022
Författare
Stråhle, Daniel
Wingård Olsson, Kevin
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Accurate 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.
Beskrivning
Ämne/nyckelord
Excavation Monitoring , CNN , Faster R-CNN , RPN , Range Sensor , Stereo Camera , Computer Vision , Material Classification