Hybrid ABC-ANN for pavement surface distress detection and classification

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Abstract

Pavement condition assessment plays an important role in the process of road maintenance and rehabilitation. However, the traditional road inspection procedure is mostly performed manually, which is labor-intensive and time-consuming. The development of automated detection and classification of distress on the pavement surface system is thus necessary. In this paper, a pavement surface distress detection and classification system using a hybrid between the artificial bee colony (ABC) algorithm and an artificial neural network (ANN), called “ABC-ANN”, is proposed. In the proposed method, first, after the pavement image is captured, it will be segmented into distressed and non-distressed regions based on a thresholding method. The optimal threshold value used for segmentation in this step will be obtained from the ABC algorithm. Next, the features, including the vertical distress measure, the horizontal distress measure, and the total number of distress pixels, are extracted from a distressed region and used to provide the input to the ANN. Finally, based on these input features, the ANN will be employed to classify an area of distress as a specific type of distress, which includes transversal crack, longitudinal crack, and pothole. The experimental results demonstrate that the proposed approach works well for pavement distress detection and can classify distress types in pavement images with reasonable accuracy. The accuracy obtained by the proposed ABC-ANN method achieves 20 % increase compared with existing algorithms.

Keywords

Pavement surface distress detection and classification Image segmentation Optimal threshold selection Maximization of entropy energy Artificial bee colony Artificial neural network 

Notes

Acknowledgments

This work is partially supported by the Faculty of Engineering at Si Racha, Kasetsart University Si Racha Campus.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Computational Intelligence Research Laboratory (CIRLab), Computer Engineering Department, Faculty of Engineering at Si RachaKasetsart University Sriracha CampusChonburiThailand

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