  With the rapid growth of display devices, quality inspection via machine
vision technology has become increasingly important for flat-panel displays
(FPD) industry. This paper discloses a novel visual inspection system for
liquid crystal display (LCD), which is currently a dominant type in the FPD
industry. The system is based on two cornerstones: robust/high-performance
defect recognition model and cognitive visual inspection service architecture.
A hybrid application of conventional computer vision technique and the latest
deep convolutional neural network (DCNN) leads to an integrated defect
detection, classfication and impact evaluation model that can be economically
trained with only image-level class annotations to achieve a high inspection
accuracy. In addition, the properly trained model is robust to the variation of
the image qulity, significantly alleviating the dependency between the model
prediction performance and the image aquisition environment. This in turn
justifies the decoupling of the defect recognition functions from the front-end
device to the back-end serivce, motivating the design and realization of the
cognitive visual inspection service architecture. Empirical case study is
performed on a large-scale real-world LCD dataset from a manufacturing line
with different layers and products, which shows the promising utility of our
system, which has been deployed in a real-world LCD manufacturing line from a
major player in the world.
