Transfer-Learning-Based Online Mura Defect Classification

Flat panel displays, such as the thin film transistor liquid crystal display, the organic light-emitting diode, and the polymer light-emitting diode, have been widely applied in many fields in recent decades. To ensure the quality of these displays, defect inspection is crucial. Mura defects, which are phenomena of uneven screen displays, are the most challenging visual defects to detect. This paper presents an online sequential classifier and transfer learning (OSC-TL) method for the online training and classification of Mura defects. OSC-TL is a new method that combines a deep convolutional feature extractor and a sequential extreme learning machine classifier. It makes online sequential training in a production line possible. To demonstrate the performance of the OSC-TL method, several experiments are performed to compare the results of this method with those of other popular classification algorithms. The experimental results show that the computational resources and time consumed by OSC-TL are well below those of other common methods because of the feature transfer and the online sequential classification strategies. Consequently, the OSC-TL method has been implemented in our automated optical inspection equipment to perform online Mura defect classification. It is able to learn and recognize a Mura defect image within 1.5 milliseconds.