Improvement of recognition rate using data augmentation with blurred images

This study aims to improve the recognition rate of a recognizer for detecting cracks on concrete surfaces from image data. When developing a recognizer, a clean image is commonly used as training data. Therefore, the recognition rate decreases when blurred images not taken cleanly are used as test data. To improve the recognition rate, the training data were mixed with processed images. In experiment, a simple convolutional neural network was used to confirm the effect of data augmentation. Experimental results showed that adding blurred images to the training data improved the recognition rate for blurred images, regardless of how the images were blurred. For training data without blurred images, the recognition rate for strongly blurred images is about 75%. On the other hand, the recognition rate improves to about 95% for training data that includes strongly blurred images. Thus, including images in which strong blur filters are used for data augmentation is effective in improving the recognition rate of blurred images.