Izvorni znanstveni članak
Automatic Pavement Crack Recognition Based on BP Neural Network
Li Li
; 1. Key Laboratory of Road and Traffic Engineeringof the Ministry of Education, Tongji UniversityShanghai 201804, China2. Jiangxi Ganyue Expressway Co., LtdNanchang 330025, China
Lijun Sun
; Key Laboratory of Road and Traffic Engineeringof the Ministry of Education, Tongji UniversityShanghai 201804, China
Guobao Ning
; School of Automotive Studies Tongji UniversityShanghai 201804, China
Shengguang Tan
; Jiangxi Ganyue Expressway Co., LtdNanchang 330025, China
Sažetak
A feasible pavement crack detection system plays an important role in evaluating the road condition and providing the necessary road maintenance. In this paper, a back propagation neural network (BPNN) is used to recognize pavement cracks from images. To improve the recognition accuracy of the BPNN, a complete framework of image processing is proposed including image preprocessing and crack information extraction. In this framework, the redundant image information is reduced as much as possible and two sets of feature parameters are constructed to classify the crack images. Then a BPNN is adopted to distinguish pavement images between linear and alligator cracks to acquire high recognition accuracy. Besides, the linear cracks can be further classified into transversal and longitudinal cracks according to the direction angle. Finally, the proposed method is evaluated on the data of 400 pavement images obtained by the Automatic Road Analyzer (ARAN) in Northern China and the results show that the proposed method seems to be a powerful tool for pavement crack recognition. The rates of correct classification for alligator, transversal and longitudinal cracks are 97.5%, 100% and 88.0%, respectively. Compared to some previous studies, the method proposed in this paper is effective for all three kinds of cracks and the results are also acceptable for engineering application.
Ključne riječi
crack detection; background correction; image processing; image recognition; BP neural network
Hrčak ID:
124150
URI
Datum izdavanja:
28.2.2014.
Posjeta: 2.351 *