A bridge crack image detection and classification method based On climbing robot

Abstract

Traditional bridge crack detection methods are of high cost and high risk. We propose a bridge crack detection and classification method based on a climbing robot using image analysis with a miniature camera mounted on the robot to collect images. First, the motion blur of acquired image is removed by Wiener filtering method. Second, wavelet transform is used to enhance fracture of the crack in the image. Third, to complete the crack image recognition, the surface morphology analysis is applied to extract crack fragments and then KD-tree is used to connect them. Finally, support vector machine method is used to classify cracks based on a series of basic visual characteristics and geometric features. Compared with geometrical characteristic classification method and BP neural network classification method, our results show that the proposed method has a better perform in crack image recognition.