Evaluation of Two Automated Thresholding Techniques for Pavement Images

by Mohamed S. Kaseko, Univ of California, Irvine, United States,
Stephen G. Ritchie, Univ of California, Irvine, United States,
Zhen-Ping Lo, Univ of California, Irvine, United States,



Document Type: Proceeding Paper

Part of: Infrastructure: Planning and Management

Abstract:

Thresholding of pavement images is an important step towards the design of an automated pavement crack detection system. However, traditional automated thresholding techniques generally do not perform well on pavement images. Recently, a number of studies have been conducted proposing different approaches for this thresholding problem. This paper presents a comparative analysis and evaluation of the performance of one of the most promising of these approaches, the regression analysis approach based on artificial neural networks.

Subject Headings: Regression analysis | Pavement condition | Automation and robotics | Pavement design | Network analysis | Cracking | Neural networks

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