Application of neural network with discreteness analysis in pavement crack identification

Xifa Song; Changyu He; Changwen Lu

doi:10.1117/12.2506005

8 February 2019 Application of neural network with discreteness analysis in pavement crack identification

Xifa Song, Changyu He, Changwen Lu

Proceedings Volume 10843, 9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging; 108430N (2019) https://doi.org/10.1117/12.2506005
Event: Ninth International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT2018), 2018, Chengdu, China

Abstract

A neural network pavement crack identification method combined with discreteness analysis is proposed. After grey transformation, image enhancement, the images are divided to two groups, one for 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 and normal roads are taken as negative samples. The two samples are used for extracting features, and the features are used to training model, and the model is used to recognize the crack in test group. For little error recognition points, a discreteness analysis was proposed to solve this problem. The contrast recognition of clean and shadowed pavement in gray value method and our method was carried out on asphalt and cement pavement respectively. Experimental result shows that the traditional gray value method is of little difference to neural network method combined with discreteness analysis in clean road, while big difference in shadow road.

Citation Download Citation

Xifa Song, Changyu He, and Changwen Lu "Application of neural network with discreteness analysis in pavement crack identification", Proc. SPIE 10843, 9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging, 108430N (8 February 2019); https://doi.org/10.1117/12.2506005

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KEYWORDS

Roads

Neural networks

Cements

Image processing

Error analysis

Image enhancement

Image filtering

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