An innovative Primary Surface Profile-based three-dimensional pavement distress data filtering approach for optical instruments and tilted pavement model-related noise reduction. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Li, Wei; Huyan, Ju; Tighe, Susan L.; Shao, Na-na; Sun, Zhao-yun
• Is Part Of: Road materials and pavement design. Volume 20:Issue 1(2019)
• Page Start: 132
• Page End: 150
• Publication Date: 2019-01-02
• Subjects: pavement 3D data -- data filtering -- Primary Surface Profile (PSP) -- biphasic standard deviation average filtering; Road materials -- Periodicals; Highway engineering -- Periodicals; Pavements -- Design and construction -- Periodicals; 625.805
• DOI: 10.1080/14680629.2017.1378118
• Languages: English
• ISSNs: 1468-0629

Abstract

Abstract : The automatic pavement management system has the advantage of providing reliable pavement maintenance and rehabilitation strategies aiming at prolonging existing pavement service life. Therefore, the quality of noise reduction results, which is an unavoidable process of automatic pavement assessment evaluation, has a significant influence on the reliability of pavement maintenance operations suggested. The primary purpose of this paper is to propose an innovative three-dimensional (3D) pavement image-based data filtering protocol, thereby maintaining a highly functional pavement surface. First, a 3D pavement depth data collection system was developed using laser light and a charge-coupled device camera. After that, based on the analysis of Positive Noise and Negative Noise, which are optical instrument-related noises, and tilted pavement model noise, the Primary Surface Profile (PSP)-based raw data filtering approach was proposed which aims at improving the noise reduction quality. Validation experiments were conducted using both the proposed approach and the traditional data filtering method, and the results show that for the not tilted pavement surface model, the PSP-based filter method can achieve the highest noise reduction value (NRV), whereas for the tilted pavement surface model, with a slightly lower NRV than that of biphasic standard deviation average filtering, which demonstrates that the proposed data filtering method has self-adaptive and robust data filter advantages which can be incorporated into a high-performance pavement performance evaluation and management system.