  Understanding assembly instruction has the potential to enhance the robot s
task planning ability and enables advanced robotic applications. To recognize
the key components from the 2D assembly instruction image, We mainly focus on
segmenting the speech bubble area, which contains lots of information about
instructions. For this, We applied Cascade Mask R-CNN and developed a
context-aware data augmentation scheme for speech bubble segmentation, which
randomly combines images cuts by considering the context of assembly
instructions. We showed that the proposed augmentation scheme achieves a better
segmentation performance compared to the existing augmentation algorithm by
increasing the diversity of trainable data while considering the distribution
of components locations. Also, we showed that deep learning can be useful to
understand assembly instruction by detecting the essential objects in the
assembly instruction, such as tools and parts.
