Development of computerized wood colour sorting system for Malaysian wood industry / Liew Shaer Jin
Wood colour sorting is essential in woodworking to maintain uniformity and consistency in the appearance of the final products, thus, improving consumer satisfaction. Majority of the wood manufacturing companies in Malaysia are depending heavily on manual colour sorting that solely relies on huma...
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| Format: | Thesis |
| Published: |
2024
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| Online Access: | http://studentsrepo.um.edu.my/15611/2/Liew_Shaer_Jin.pdf http://studentsrepo.um.edu.my/15611/1/Liew_Shaer_Jin.pdf http://studentsrepo.um.edu.my/15611/ |
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| Summary: | Wood colour sorting is essential in woodworking to maintain uniformity and
consistency in the appearance of the final products, thus, improving consumer
satisfaction. Majority of the wood manufacturing companies in Malaysia are depending
heavily on manual colour sorting that solely relies on human visual inspection, which can
be subjective, inconsistent, laborious, and subject to errors. Automation is a goal,
however, the cost for implementation of established technologies is always extortionate
especially for small and medium industries (SMI). Therefore, the aim of this research is
to develop a computerized vision system to perform colour sorting for multi-scale
woodworking facilities. To achieve the research goal, our objectives are set to determine
a suitable algorithm for colour features classification, to select the best features which
contribute the most in the classification and to compare the effect of different cameras in
the performance of the colour sorting. We have compared camera of different genres,
namely an industrial camera, a prosumer action camera, and a webcam. Three cameras
used were: i) Hikrobot® MV-CE200-10UC (CE200), ii) Logitech® C920 HD Pro
(C920), and iii) Sony® RX0 II (RX0 II). After setting up a veneer imaging prototype, a
total of 1,289 distinct images of American red oak (Quercus rubra), yellow poplar
(Liriodendron tulipifera), and maple (Acer spp.) were acquired from each camera,
summing up to 3,867 images from all cameras. After performing image preparations and
calibrations, 26 features were extracted from each image. The features were based on the
average and standard deviation of the wood basal colour and wood grain colour. Salient
features were obtained using Sequential Forward Selection (SFS), which were then used
to train a Self-Organizing Map (SOM). The results affirmed that the colour of the basal
colour is highly correlated with human sorted colour groups. As expected, CE200
performed the best being of industrial grade. Interestingly, C920 exhibited comparable
performance to CE200. RX0 II performed the worst due to its interface software limitations. This proposed system achieved accuracies of 89.0% for red oak, 94.3% for
yellow poplar and 96.4% for maple. This research will assist the SMI to develop
affordable vision systems for colour sorting.
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