| Summary: | Asphalt cracks are one of the major road damage problems in civil field as it may potentially threaten the road and highway safety. Crack detection and classification is a challenging task because complicated pavement conditions due to the presence of shadows, oil stains and water spot will result in poor visual and low contrast between cracks and the surrounding pavement. In this paper, the network proposed a fully automated crack detection and classification using deep convolution neural network (DCNN) architecture. First, the image of pavement cracks manually prepared in RGB format with dimension of 1024x768 pixels, captured using NIKON digital camera. Next, the image will segmented into patches (32x32 pixels) as a training dataset from the original pavement cracks and trained DCNN with two different filter sizes: 3x3 and 5x5. The proposed method has successfully detected the presence of crack in the images with 98%, 99% and 99% of recall, precision and accuracy respectively. The network was also able to automatically classify the pavement cracks into no cracks, transverse, longitudinal and alligator with acceptable classification accuracy for both filter sizes. There was no significant different in classification accuracy between the two different filters. However, smaller filter size need more processing training time compared to the larger filter size. Overall, the proposed method has successfully achieved accuracy of 94.5% in classifying different types of crack. © Published under licence by IOP Publishing Ltd.
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