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In this paper, we propose a preprocessing method of exploiting noise and blur for effective noise elimination in data. At present, there are many kinds of research to improve the performance of object classification, detection, and image segmentation based on deep learning. For instance, adding noise to data, multiple in-depth convolution layers, and data augmentation have been studied in many ways. An in-depth convolution network results in long processing time and data augmentation gives a burden to memory usage. However, adding noise and blur data preprocessing method gives less burden to hardware, which helps improve algorithm performance. The proposed method is applied to TFT-LCD (Thinfilm Transistor Liquid Crystal Display) PAD defect detection for improved performance. To verify the accuracy and repeatability, 691 actual defect images are used in experiments. These images are composed of complex patterns and defects in the images having barely 2 pixels with little intensity difference. To confirm which filters are better, Gaussian blur, Salt & Pepper noise, and Gaussian noise filters are used for comparison. According to the result, the experiments with Salt & Pepper and Gaussian noise detect all defects. However, the repeatability of the Gaussian noise filter seems better than that with Salt & pepper. Furthermore, applying noise and blur to train data shows more than twice higher detection accuracy than those without such applications. We verified that using Gaussian noise and blur indicates excellent accuracy and repeatability when inspecting the TFT-LCD PAD area in AOI machines.
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