Research Article Asphalt Mixture Segregation Detection: Digital Image Processing Approach Mohamadtaqi Baqersad, Amirmasoud Hamedi, Mojtaba Mohammadafzali, and Hesham Ali Department of Civil and Environmental Engineering, Florida International University, 10555 West Flagler Street, Miami, FL 33174, USA Correspondence should be addressed to Mohamadtaqi Baqersad; mbaqe001@fu.edu Received 13 December 2016; Accepted 20 March 2017; Published 5 April 2017 Academic Editor: Luigi Nicolais Copyright © 2017 Mohamadtaqi Baqersad et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Segregation determination in the asphalt pavement is an issue causing many disputes between agencies and contractors. Te visual inspection method has commonly been used to determine pavement texture and in-place core density test used for verifcation. Furthermore, laser-based devices, such as the Florida Texture Meter (FTM) and the Circular Track Meter (CTM), have recently been developed to evaluate the asphalt mixture texture. In this study, an innovative digital image processing approach is used to determine pavement segregation. In this procedure, the standard deviation of the grayscale image frequency histogram is used to determine segregated regions. Linear Discriminate Analysis (LDA) is then implemented on the obtained standard deviations from image processing to classify pavements into the segregated and nonsegregated areas. Te visual inspection method is utilized to verify this method. Te results have demonstrated that this new method is a robust tool to determine segregated areas in newly paved FC9.5 pavement types. 1. Introduction Characteristic of asphalt material impacts on asphalt qual- ity and driving safety [1]. Segregation of aggregates alters material properties and accelerates deterioration’s rate [2, 3]. Pavement segregation is one of the main concerns afecting the performance of asphalt pavements. Segregation is defned as separation of aggregates gradation, so that coarse and fne aggregates are separated in the asphalt mixture [4]. Also, existence of enough bituminous and stickiness between asphalt materials may reduce deterioration of asphalt con- crete [5]. Detection of segregated areas in pavements has always been a disputable issue between agencies and con- tractors. Segregated areas are primarily detected by the visual inspection and verifed by core density [4]. Segregation can also be determined by measuring the surface texture of the pavement. Florida Texture Meter (FTM) and Circular Track Meter (CTM) are laser-based devices that can be used for this purpose [6, 7]. Image processing is another tool that is used to qualify pavement texture [8]. Scanners and cameras are two types of digitalizing devices to produce a 3D image model [9]. Digitalizing can be used to measure diferent size and scale objects [10]. Blais (2004) developed a scanning machine, which captures 3D data from the surface, and then cat- egorized the road texture using two algorithms [10]. Te frst algorithm calculates the estimated texture depth (ETD), and the second calculates the texture profle level (TPL). Te measurements from nine pavements were collected to validate these algorithms. Te results show that there is a good agreement between the traditional Sand Patch method results and the 3D scanning prototype equipment [11]. Te feasibility of using image processing to determine the gradation without separation of bitumen and aggregates was studied by Bruno et al. [12]. Te image, at frst, subdivided into diferent distinct areas where each distinct area has homogeneity with the area around it. Tis procedure helps to separate voids, aggregates, and bitumen areas. Frequency histogram of each distinct area has been used to detect the gradation of the asphalt mixture. Dividing the image into distinct regions can also be used to detect pavement distresses [13]. In this case, the transform method to designate lines Hindawi Advances in Materials Science and Engineering Volume 2017, Article ID 9493408, 6 pages https://doi.org/10.1155/2017/9493408