Several techniques have been used in the literature for detecting cracks such as thresholding, morphological operations, edge detection, disparity transforms, and so on. In this work, a technique based on convolutional neural network (CNN) is developed and applied. In the presented approach, the pixels of the image classified as cracks are combined and fused together, which leads to high-resolution black and white image. The proposed technique can be used in real time for pavement distress detection and used for road maintenance and conditioning. The details of the proposed technique are shown in Sections 2 and 3.
Crack detection is a challenging image processing task, which is crucial in pavement engineering. It is a real-time image processing and analysis technology that monitors and locates cracks on a pavement. In the past decades, many researchers have applied various image processing methods to detect cracks on pavement. Some of the examples include: edge detection , neural network , and region growing . The key problem in pavement crack detection is to classify pixels of a crack from those of the pavement. The state-of-the-art image processing techniques are designed for pavement crack detection . To improve the crack detection performance, in recent years, researchers have developed a variety of machine learning models in pavement crack detection, such as classic SVM , support vector clustering , support vector learning , and so on. A key issue in pavement crack detection is to distinguish crack pixels from those of the pavement, and many researchers have applied various feature extraction approaches to this task . For example, with the help of histograms, Gabor filters, canny edge detection, etc. Image texture is one of the most important features for crack detection. For example, for crack detection [2, 4], the authors proposed to compare the texture features of crack image and pavement image. In , the authors proposed a technique for extracting texture features using a modified Gabor filter and a set of standard mathematical operations. Another example of crack detection is a machine learning approach [1, 9].
Section 4 introduces the related works on pavement distress and pavement crack detection using computer vision methods. Section 5 explains the proposed work as well as the experimental setup. The results of the proposed work are demonstrated in Section 6 and the work is concluded in Section 7.
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