Issue 30

W. Tao et alii, Frattura ed Integrità Strutturale, 30 (2014) 537-544; DOI: 10.3221/IGF-ESIS.30.64 544 C ONCLUSIONS irstly, this paper collects crack images to conduct spatial filtering after graying and enhancing the images with the neighborhood average method. Then it performs gray-level threshold segmentation combined with the iterative method. Finally, information on the cracks is extracted based on the underwater images of the bridge. Our aim is to identify the areas of cracks in the image and obtain the parameters of the cracking region, such as area, length, width and perimeter. This paper, therefore, extracts and inspects the crack parameters as follows. The circularity of the crack target is small; the major-minor axis ratio of the external ellipse is large. The crack area’s grayscale average is smaller in the grayscale images, while the external rectangle’s grayscale average is larger. 0 value is generated in the calculation results of the characteristic quantities. The target was divided into discontinuous fragment regions after extracting the target value. We need to connect the fragment area establishing models and instance validation. Finally we will find that it is correct and valid to connect the fracture fragments by applying this algorithm. Application of the median filtering method to enhance denoising can have better denoising effect. And it could smooth the image after enhancing denoising and reduce the data volume of the monitoring stream with compressed synthesis. The neighborhood average method is a common technology for image denoising. Its advantage is rapid processing speed and wide range of application. But this algorithm also has a significant shortcoming, namely, that the image will become vague when reducing the noise at the same time, especially in the image's edges and details. It is therefore necessary to make some improvements to the simple neighborhood average method. Another key solution is keeping the image’s edges and details as much as possible to a minimum when performing image denoising. R EFERENCES [1] Juwu, X., The research of application and measurement the bridge cracks based on digital image processing, Southwest Petroleum University, 5 (2011). [2] Wei, Z., The research of underwater image segmentation and recognition technology based on particle swarm, Harbin Engineering University, 9 (2008). [3] Hanxing, Q., The research of pavement crack detection and recognition based image segmentation. Chongqing Jiao tong University, 03 (2012). [4] Shaobo, B., Maode, Y., Liang jun, H., Yuyao, H., Research on measuring algorithm of asphalt pavement crack based on image processing, Computer Measurement and Control, 15(10) (2007) 1305-1307. [5] Xiaorui, L., Xiongyao, X., The research of rapid detection of tunnel surface cracks based on image processing, Chinese Journal of Underground Space and Engineering, 2 (02) (2009) 1624-1628. [6] Jun, F., Weiliang, J., Feng, K., Yunhua, C., Detection of shallow cracks in the wall with digital image processing, civil Architecture and Environmental Engineering, 31 (06) (2009)137-141. [7] Lto, A., Aoki, Y., Hashimoto, S., Accurate extraction and measurement of fine cracks from concrete block surface image, IECON02, 3 (2002) 2202-2207. [8] Kawamura, K., Miyamoto, A., Nakamura, H., Sato, R., Proposal of a crack pattern extraction method from digital images using an interactive genetic algorithm, Proceedings-Japan Society of Civil Engineers, (2003) 115-132. [9] Abdel, Q., Abudam, O., Kelly, M. E., 2003. Analysis of edge detention techniques for creak identification in bridges, Journal of Computing in Civil Engineering, 17(3) (2003) 255-263. [10] Guoqi, Z., The research of concrete bridge underside crack detection method based on image processing, Beijing Jiao tong University, 06 (2010). [11] Galvez, J. C., Cervenka, J., Cendon, D. A., Saouma, V., A Discrete Crack Approach to Normal/Shear Cracking of Concrete, Cement and Concrete Research, 32(10) (2002) 1567-1585. [12] Xiaorui, L., Xiongyao, X., Rapid Crack Inspection of Tunnel Surface Based on Image Processing, Chinese Journal of Underground Space and Engineering, 5(02) (2009) 1624-1628. [13] Xiaomao, R., A technology of screen translation, Computer and Digital Engineering, 2(22) (1994) 44-45. [14] Ciyin, Y., Lianqing, H., Contrast enhancement of medical image based on since grey level transformation, Optical Technique, 5 (28) (2002) 407-408. [15] Xu, B., A Study on the Bridge Diseases Inspection and the Cracks Measurement Based on the Imagery Processing Technology, Chang'an University, (2009). F