Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet

Abstract

In this research, 200 corrosion images of steel and 500 crack images of rubber bearing are collected and manually labeled to build the data set. Then the two data sets are respectively adopted to train VGG-Unet models in two methods, aiming to conduct Damage Segmentation by inputting different size of data set. One method is Squashing Segmentation to input squashed images from high resolution directly into VGG-Unet model while Cropping Segmentation uses cropped image with size 224 × 224 as input images. Because the proportion of damage pixels in the data set is different, the results produced by the two data sets are quite different. For large size damage (such as corrosion) segmentation, Cropping Segmentation has a better result while for minor damage (such as crack) segmentation, the result is opposite. The main reason is the gap in the concentration of valid data from the data set. To improve the capability of crack segmentation based on Cropping Segmentation, Background Data Drop Rate (BDDR) is adopted to reduce the quantity of background images to control the proportion of damage pixels from the data set in pixel-level. The ratio of damage pixels from the data set can be decided by different value of BDDR. By testing, the accuracy of Cropping Segmentation becomes relatively higher under BDDR being 0.8.

Keywords

• fully convolutional network
• damage segmentation
• VGG-Unet
• pixel-level data balance