With the trend of industry 4.0 in recent years, how to
apply Smart manufacturing into product lines has become
the subject greatly focused in the industry. Nowadays,
in the product line of wooden product processing, such
as wood panel and wood veneer, most defect detection is
still proceeded artificially. Product defect detection
is one of the key part in the product lines. Thus, if we
can introduce Automated Optical Inspection into product
lines, this will be a great point to head for
intellectualizing product lines. However, for the
detected target like woods whose exteriors are
complicated and nonregulated, it’s hard to find out the
defects via traditional computer vision algorithm. With
the rapid development of artificial intelligence and the
technology of deep learning, many object detection model
have been proposed. The kind of models has high
adaptability and capabilities of finding targets from
the messy background. To applying them as a measure of
detecting defects possesses outstanding potential. Yet,
object detection models needs a great amount of image
training to have better detecting results. Within
practical experiences, it’s quite common to face that
not enough samples of products results in unideal
detecting results. If we could overcome the problems,
the possibility of applying the object detection models
to the product lines will be elevated significantly.
The research will practically apply the object detection
model, evaluate whether it suits for the defects
detection of wooden exteriors or not, and study the
differences of detection effects on the different
Feature Extractor. Besides, the research will use the
image generating model to generate images of wooden
defects, testing if training with image generating
together with fewer original samples can elevate the
defect detection effects on the object detection models.
The research will eventually propose a best detecting
set for wooden exterior defects according to the final
result.
Through the tests of the research, under the situation
with enough samples, the training result of Faster R-CNN
has almost 60 percent recall no matter we chose VGG16,
Resnet50 or InceptionV2 as feature extractors. Under the
situation with fewer samples, adding the generated
defect images of Pix2Pix or GuaGan to the training will
elevate the recall. For instance, training InceptionV2
as feature extractor with the defect images generated by
GuaGan together can elevate the recall from 41.58% to
89.34%, which presented a significant effect on
improving the results. Thus, using the set as a measure
for the detection for wooden exterior defects showed a
high potentiality.
Keywords:
Smart Manufacturing,
Artificial Intelligence, Deep Learning, Object,Detection, Image Generating,
Wood Surface Defect