Colour sorting of red oak, yellow poplar and maple veneers using self-organizing map: comparisons between different camera genres

Colour sorting is a vital process in manufacturing of high-quality wood products. It is however a manual process in a large majority of manufacturing facilities in Malaysia. Automation is an ideal solution; however, costs are prohibitive for small and medium industries (SMI). This project aims to produce a flexible solution that can cater for manufacturers of different scales. Three cameras of different price ranges were used: (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, human sorted images of American red oak (Quercus rubra), yellow poplar (Liriodendron tulipifera), and maple (Acer spp.) were acquired. 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 poplar and 96.4% for maple. This research will assist the SMI to develop affordable vision systems for colour sorting.