Bolt-Loosening Detection using Vision-Based Deep Learning Algorithm and Image Processing Method

BACKGROUND: Grape seed proanthocyanidin extract (GSPE) has a certain resistance to contrast light, which makes the boundary of the imaging image unclear. OBJECTIVE: Because of this, an image processing algorithm is needed to process the contrast image to study the role of GSPE in the process of anti-ultraviolet. METHODS: In this paper, the fuzzy edges of contrast images were processed by deep learning algorithm, and the changes of VEGF and PEDF expression in HaCaT cells before and after UVA irradiation and after GSPE intervention were studied. RESULTS: The experiment results show that after processing, the edge and boundary of the image become clear and separable, which can be used to compare and analyze the test process. The image comparison results show that GSPE can down regulate the expression of VEGF gene and protein, and up regulate the expression of PEDF gene and protein. The synergistic effect of GSPE and GSPE can inhibit angiogenesis. It is confirmed that GSPE has the effect of anti-ultraviolet ray induced early angiogenesis.

Download Full-text

Study of filming condition for deep learning based crack detection method

10.5592/co/cetra.2020.1059 ◽

2021 ◽

Author(s):

Tomotaka Fukuoka ◽

Takahiro Minami ◽

Makoto Fujiu ◽

Junichi Takayama

Keyword(s):

Image Processing ◽

Deep Learning ◽

Service Life ◽

Visual Inspection ◽

Crack Detection ◽

Detection Method ◽

Processing Method ◽

Inspection Method ◽

Image Processing Method

Recently, the study of extending the service life of bridges has gained attention. In Japan, there are about 730,000 bridges with a length of 2 m or longer, and many of these were built during a period of high economic growth, and have now reached the end of their service life. Therefore, their rebuilding and the extension of their service life must be considered. However, some local public organizations have problems that insufficient manpower relative to the number of bridges to manage, as well as insufficient funding for maintenance. Thus, these organizations are unable to perform routine close visual inspections. Specific problems include “notably less staff and consulting technicians relative to the number of bridges to be managed” and “high inspection cost preventing from funding for repair.” As issues with the continuing close visual inspection of bridges are surfacing, the remote imaging system is expected to become a new inspection method that replaces close visual inspection. The practical potential of bridge inspections using images captured with a super-high-resolution camera was examined. A super-high-resolution camera enables us to take a wide area picture of a target bridge from a long distance. An image processing method could improve the efficiency of image-based inspection method. For example, a deep learning-based image processing method could extract a damaged area on a surface of a bridge automatically with high accuracy faster than human inspection. In general, the accuracy of an image processing method is affected by the quality of an input image. Filming conditions are one of the factors that determine the quality of a photo image. It is important to evaluate the effect of filming conditions to improve the reliability of an image processing method. In this paper, we evaluate the effect of the filming conditions for an image processing method by comparing the results of a deep learning-based crack detection method.

Download Full-text