# Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet
## 6 Pixel-Level Data Balance
### Results  
All accuracy outcomes of different data set under different BDDR by different evaluation methods are shown in Table 6. In the Table, it can be found that with increasing of BDDR, MPA and MIoU increase a lot while PA and FWIoU don't change obviously, because MPA and MIoU enlarge the weight of damage pixels as same as the weight of non-damage pixels. This proves that more reasonable BDDR could improve the capability of predicting damages from images of rubber bearings and evaluation method to increase the weight of objects is necessary especially the objects occupy few pixels of whole data set, such as MPA and MIoU. The results of all images are better than which exclude images without damages. This is because of the lower percentage of pixels of damage after taking images without damages into account. Since most of rubber bearings are intact while inspection in real life, the results would be much higher than those in Table 6.  
While setting \(x\) is equal to 0.1, RWIoU0.1 shows similar result as MIoU. And results FWIoU and RWIoU0.1 under BDDR of 0.8 are all higher than these under BDDR of 0.9, while FWIoU is a little lower. This is because the VGG-Unet model trained from Cropping Segmentation under BDDR of 0.9 is too sensitive to damages that it sometimes misjudges pixels near damages as damage pixels, even though it may have a better performance of predicting background. And enlarging weight of damages cause the pervious evaluation results. As comparing MIoU results of all images and images with damages, when about half of images including damages, MIoU result has 18% difference while RWIoU0.1 has 13.5% difference. This difference would be lower if \(x\) is larger. Since using RWIoU could decrease the difference, under condition of different damage image proportion, it could be taken for accuracy evaluation for segmentation especially in real life. Figure 12 shows some typical examples for rubber bearing inspection. (a) is ground truth, which means manually labeled images, and from (c) to (g) are results of Cropping Segmentation under different BDDR. Image A is the image of seriously damaged rubber bearing, and it is taken almost from a positive angle to the surface of rubber bearing and very similar to the images in the traditional data set for segmenting damages. Without too many interferences of background, image A could show the capability of damage prediction. Image B simulates the angle of view while inspect structures in the narrow space, since the filming angle is very small from the object's surface and the distances of rubber surface to camera lens are different. Image C is the rubber bearing photo taken in a long distance and in it the rubber bearing only occupy a small area. And Image D shows one wide crack on the rubber bearing, and it is taken with the condition that half of pixels are from background, such as trees and buildings. This condition would interfere the results of object recognition and generate some noise. These images prove that with BDDR increases, the capability of damage prediction becomes stronger and stronger, with nearly most pixels of damages could be predicted and labeled. However, in the training data set, the VGG-Unet models are also a little more sensitive to background, especially pixels on the boundaries, in darker colors or under lower illuminance. Thus, from Figure 12 it proves that segmentation results are exact while BDDR is 0.8. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Value of BDDR** & **All** & **Training** & **Validation** & **Testing** \\ \hline
0 & 0.008 (0.130) & 0.007 (0.123) & 0.016 (0.194) & \\
0.2 & 0.009 (0.130) & 0.009 (0.123) & 0.019 (0.194) & \\
0.5 & 0.014 (0.130) & 0.013 (0.123) & 0.029 (0.194) & 0.005 (0.081) \\
0.8 & 0.024 (0.130) & 0.028 (0.123) & 0.059 (0.194) & \\
0.9 & 0.033 (0.130) & 0.046 (0.123) & 0.091 (0.194) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MDR of data set after dropping background data.