Share Email Print
cover

Proceedings Paper • new

Application of neural network with discreteness analysis in pavement crack identification
Format Member Price Non-Member Price
PDF $17.00 $21.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 8 February 2019
PDF: 10 pages
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); doi: 10.1117/12.2506005
Show Author Affiliations
Xifa Song, Highway Science Research Institute of the Ministry of Transportation (China)
Changyu He, China Electronic Science Research Institute (China)
China Electronics Technology Group Corp. (China)
Changwen Lu, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 10843:
9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging
Yadong Jiang; Xiaoliang Ma; Xiong Li; Mingbo Pu; Xue Feng; Bernard Kippelen, Editor(s)

© SPIE. Terms of Use
Back to Top