# Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet
## 1 Introduction  
The collapse of I-35W Mississippi River bridge in America in 2007, killed 13 people, injured 145 people and led to millions of economic losses in dollars [1]. The main reason of the accident is significant corrosion in its bearings caused by the lack of regular inspection and repair work. Furthermore, in 2019, the Nanfang'ao bridge in Taiwan collapsed, killing 6 people and injuring 12, due to negligence of long-term detection and maintenance. The damages on the components may be very ordinary but strongly affect the functions of components. These components seriously influence the capacity of the bridge and even endanger the safety of the bridge. Thus, bridge management is becoming one of the major issues to keep their infrastructures'esthetic and durability.  
However, it is quite difficult for some countries to solve the issue. For example, about 40% of over 61,000 bridges are 50 years or even older in America, according to infrastructure report card of American Civil Engineering Society (ASCE). In Japan, many infrastructures such as tunnels and bridges which are constructed during the period from 1954 to 1973, nowadays are facing many ageing deterioration problems [2]. This ageing deterioration issue is concerned not only in developed countries, but also in some developing countries.  
In China, 40% of bridges are over 25 years while the maintenance cannot involve all these bridges, according to statistics from experts [3]. Due to the count of aged bridges which need to be inspected is large, efficiency is an important factor to solve the problem.  
The traditional method, manual inspection, according to the observed sizes and locations of damage [4], takes a lot of operation time, expensively spends for the special inspection vehicles and needs to constrain or stop the transportation. The innovative method applying Unmanned Aerial Vehicles (UAVs) or robots could collect images fast for civil infrastructures [5], and damage detection is available for some special structure members where are difficult to access by traditional manual inspection. This would obviously fasten the process of diagnosing the status of the detected infrastructure. Both traditional and innovative methods, it is necessary to mark the damages to evaluate the status of inspected structures after collecting pivotal images of rubber bearings. However, the operation also costs large amount of human processing hours.  
To address this issue, a lot of methods based on image processing to detect damages or structures have been proposed [6]. Image Processing Techniques (IPTs) is one of entire suite for detecting, assessing and segmenting some specific defects, once visual data of infrastructures are collected [7]. For instance, cracks or palling on concrete [8; 9]. IPTs can detect and identify damages from components images, basing on remarkable assumptions, such as cracks having darker colors and thinner patterns than background. To improve robustness of detecting damages, basic edge detection and contour detection methods [10] like fast Haar transform [11] and fast Fourier transform [12] method were reliable. Even though IPTs-based methods are shown to be fast and effective for damage identification, the performance of thresholding algorithms for noises, lighting and distortion data in images is questionable [13]. In fact, nearly all images for damage detection are taken under the real-world situations and more or less with some noises. Thus, the adaptability improvement of IPTs-based methods is desired. Continuously, Machine Learning (ML)-based approaches are conducted as feasible solutions for damage detection [14; 15].