Image-based Agarwood Resinous Area Segmentation using Deep Learning
Irwandi Hipiny Johari Abdullah Noor Alamshah Bolhassan
The manual extraction method of Agarwood resinous compound is laborious work,
requires skilled workers, and is subject to human errors. Commercial Agarwood
industries have been actively exploring using Computer Numerical Control (CNC)
machines to replace human effort for this particular task. The CNC machine
accepts a G-code script produced from a binary image in which the wood region
that needs to be chiselled off is marked with (0, 0, 0) as its RGB value.
Rather than requiring a human expert to perform the region marking, we propose
using a Deep learning image segmentation method instead. Our setup involves a
camera that captures the cross-section image and then passes the image file to
a computer. The computer performs the automated image segmentation and feeds
the CNC machine with a G-code script. In this article, we report the initial
segmentation results achieved using a state-of-the-art Deep learning
segmentation method and discuss potential improvements to refine the
segmentation accuracy.