A neural network pavement crack identification method combined with discreteness analysis isproposed. After grey transformation, image enhancement, the images are divided to two groups, onefor training, the other one for test. The images in training group are divided into a series of sub blocks.The sub blocks contain cracks are taken as positive samples, and the sub blocks with shadows andnormal roads are taken as negative samples. The two samples are used for extracting features, and thefeatures are used to training model, and the model is used to recognize the crack in test group. For littleerror recognition points, a discreteness analysis was proposed to solve this problem. The contrastrecognition of clean and shadowed pavement in gray value method and our method was carried out onasphalt and cement pavement respectively. Experimental result shows that the traditional gray valuemethod is of little difference to neural network method combined with discreteness analysis in cleanroad, while big difference in shadow road.
展开▼
