Issue 30

W. Tao et alii, Frattura ed Integrità Strutturale, 30 (2014) 537-544; DOI: 10.3221/IGF-ESIS.30.64 537 A research on detecting and recognizing bridge cracks in complex underwater conditions Wang Tao, Du Tao, Zou Xiaohong, Sun Yiming Institute of Highway, Chang’an University, Xi'an, 710064, China 48045664@qq.com A BSTRACT . The method aims to recognize and extract the characteristic parameters of bridge cracks based on images of the cracks obtained through the application of preprocessing technologies, such as graying, graphical enhancements, spatial filtering, gray-level threshold segmentation, etc.. The approach has been tested for accuracy to avoid the incorrect identification of chaff as a method error. The proposed method has proved to be rather accurate and effective in extracting information on cracks from the bridge image tests. K EYWORDS . Crack detection; Digital processing; Image analysis; Fracture fragments; Electronic information. I NTRODUCTION he first cracks start appearing at the bottom of the bridge structure. If detected in time, engineers can then follow the development of the cracks. Meanwhile, it should the bridge should be maintained and repaired without delay. Detecting and recognizing cracks in time can reduce maintenance costs greatly and effectively guarantee the security of public transportation [1]. The difficulties in detecting cracks at the bottom of bridges are as follows [2]: the underwater pressure is immense and the light is dim. Furthermore, conditions in the underwater environment can be very bad. The peculiarity of the underwater environment and the characteristics of the water can impede and obstruct communications. These difficulties combined lead to typical problems in underwater viewing. The complex imaging environment makes underwater images more sensitive to noise and interference. That inevitably leads to the generally bad quality and information redundancy of underwater images. Digital image processing technology has been widely used in the detection of underwater bridge cracks in complicated conditions. This primarily includes the preprocessing and partition of images, the extraction of of the image features, image classification and other links [3]. In China, Bo Shaobo [4] has proposed that the morphological corrosion operator should refine the crack to obtain a single-pixel width skeleton image. Meanwhile, the length and width of the cracks may be measured by applying the statistical pixel method in dealing with rule cracks in images. The encroachment method should be used to measure the area of the crack when dealing with irregular cracks. Liu Xiaorui [5] has presented a fusion method combined with several processing tectonic treatment methods-SFC. Fu Jun [6] has applied a new method of image segmentation based on a neural network. Abroad, Iota et al [7] have applied image binarization, wavelet transform, gray correction and other image processing methods to analyze the crack images and extract information. KawamuraK et al. [8] have found that the parameter genetic algorithm of image processing can be semi-automatically optimized for the effective accurate detection of cracks. Abdel-Qader [10] etc. also assessed surface crack detection efficiency at the same time. They compared the results of the Fourier transformation, Sobel filtering, Canny filtering and wavelet transform method, finding that the wavelet transform was more reliable. However, the research on underwater bridge crack image segmentation algorithms still failed to meet the development needs of bridge image detection technology. And image T