CIVIL ENGINEERING 365 ALL ABOUT CIVIL ENGINEERING



AbstractThe detection of pavement crack plays a critical role in pavement maintenance and rehabilitation because pavement cracking is one of the most important indicators for the pavement condition evaluation, as well as an early manifestation of other pavement distresses. To detect cracks accurately, precisely, and completely based on three-dimensional (3D) pavement images, this paper proposes a deep learning framework based on a convolutional neural network (CNN) and pixel-level improved crack seed algorithm, called Pavement Crack Detection Net (PCDNet). Firstly, the CNN layer based on the convolution implementation of sliding windows is applied to each 3D pavement image to divide it into 8×8 pavement patches and classify each patch into two types: the background patch, and the pavement crack patch. Secondly, the seed layer, i.e., an automatic threshold pixel-level crack seed recognition algorithm is used to detect the crack distress further and depict the complete contour simultaneously. Finally, the region growing layer is utilized to ensure the continuity of the cracks. Due to the good combination of the CNN and the algorithm, PCDNet needs only a patch-level data set for training but can output pixel-level results, a great novelty in crack detection. In this paper, 5,000 3D pavement images were selected from an established image library. PCDNet was trained with 4,300 3D pavement images and further validated based on 500 3D pavement images. The test experiment based on the remaining 200 images showed that PCDNet can achieve high precision (0.885), recall (0.902), and F-1 score (0.893) simultaneously. It also was demonstrated that PCDNet can detect different types of pavement crack under various conditions and resist noncrack pixels with elevation variation features, such as pavement edge drop-offs, curbs, spalling, and bridge expansion joints. Compared with recently developed crack detection methods based on imaging algorithms, PCDNet is capable of not only eliminating more local noise and detecting more fine cracks, but also maintaining much faster processing speed.



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