# Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet
## 7 Conclusions  
This paper uses segmentation method to detect corroisons from steel bridges and cracks from rubber bearings, based on VGG-Unet. The model to detect corroisons have a good performance while the capability of model for rubber bearing damage detection needs to be improved. Then, by evaluating the prediction results, a possibility is proposed that the proportion of pixels of each category in the data set will reduce the accuracy of damage recognition. To verify the idea and improve the model's capability of detecting rubber bearing damages, Background Data Drop Rate (BDDR) is defined to control the proportion of damage pixels from each data set. Different from traditional method to improve accuracy of models by adding more images to the data set, the data-dropping method improves the damage recognition ability of VGG-Unet models by reducing the amount of other pixels. Here summarize the conclusions as follows:  
* Squashing Segmentation is conducive to detection of the overall position of the damage. However, compared with Cropping Segmentation, this method doesn't have a good performance on detecting damages' precise location, due to some feature information are lost during the compressing process before training.
* The damage detection capability of Cropping Segmentation is largely affected by the concentration of valid data in the data set. If the percentage of damage is very low, the VGG-Unet model may be too sensitive to the background pixels, which leads to the low accuracy.
* If the data sets are with low MDR, there is a large gap between different evaluation methods that enhance the weight of damages or not. In this case, FWIoU can be used to make a balance between these different evaluation methods and reduce the gap while expending the weight of minor damage.
* BDDR is an effective parameter to control the proportion of damage pixels in the data set and improve VGG-Unet model's capability on detecting minor damages. In this research, BDDR of 0.8 has the highest accuracy, slightly stronger than BDDR of 0.9. It shows that a data set with an appropriate concentration of damage pixels is more helpful to train a higher-precision VGG-Unet model.  
In the future, more values of BDDR need to be tested to find the best proportion of damage pixels on training for Cropping Segmentation. Besides, due to the models trained in this research is only for single category of Damage Segmentation, models for multiple categories of damage detection also needs to be trained and the method of dropping data to make a balance between all categories of damage pixels is necessary to be researched.  
[PERSON] and [PERSON] contributed to methods design, data analysis and drafted the manuscript. [PERSON] conceived the technical solution. [PERSON], [PERSON], [PERSON] and [PERSON] developed the data set. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflicts of interest.