Scale insensitive and focus driven mobile screen defect detection in industry

With the wide-spread of smartphones, mobile phone screen has become an important IO device in HCI and its quality is of great matter in interaction. Traditional defect detection process involves heavy labor cost or relies on unstable low-level features and suffers from both scale and model sensitive problems. Screen defect varies in size, shape, intensity and is hard to be described. Efficient and accurate detection system remains an urgent need in mobile phone screen manufacturing. In this paper, we propose an end-to-end screen defect detection framework. A defect detection network with merging and splitting strategies (MSDDN) to deal with multiple size and shape variations of defect image patches is firstly designed. After training, feature maps of the last layer before the output of MSDDN can be regarded as good representations of a screen image patch. These feature maps are concatenated into a unified feature vector. We then train a recurrent neural network (SCN) to decide which input screen image patch in a sequence is the most likely to contain defects, where the patches are cropped from the same image, and the feature maps are used as input. As SCN emphasizes on the comparison of image patches from one image, it is less sensitive to different screen batches. The patch with the highest probability of containing defects, or called focus area, is further processed with a sliding window to fill in the MSDDN to produce the final results. Finally, to improve the efficiency of the calculation process to fulfill the real industrial demand, we perform both filter selection and weight quantization on the weights in MSDDN under the purpose of building a low-precision version network without great loss in accuracy (MSDDN-l). Experimental results show MSDDN can better handle the defect variations than traditional models and general purpose convolutional neural networks. Meanwhile, SCN can accurately predict the focus area, and MSDDN-l can greatly improve the efficiency. (C) 2018 Elsevier B.V. All rights reserved.