This paper presents a neural network based technique for the classification of segments of road images into cracks and normal images. The density and histogram features are extracted. The features are passed to a neural network for the classification of images into images with and without cracks. Once images are classified into cracks and non-cracks, they are passed to another neural network for the classification of a crack type after segmentation. Some experiments were conducted and promising results were obtained. The selected results and a comparative analysis are included in this paper.

A Neural Network based Technique for Automatic Classification of Road Cracks

Segregation determination in the asphalt pavement is an issue causing many disputes between agencies and contractors. The visual inspection method has commonly been used to determine pavement texture and in-place core density test used for verification. 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. The visual inspection method is utilized to verify this method. The results have demonstrated that this new method is a robust tool to determine segregated areas in newly paved FC9.5 pavement types.

/pdf/asphalt-mixture-segregation-detection-digital-image-4s11qdzt59.pdf

Asphalt Mixture Segregation Detection: Digital Image Processing Approach

Pavement crack types provide important information for making pavement maintenance strategies. This paper proposes an automatic pavement crack classification approach, exploiting the spatial distribution features (i.e., direction feature and density feature) of the cracks under a neural network model. In this approach, a direction coding (D-Coding) algorithm is presented to encode the crack subsections and extract the direction features, and a Delaunay Triangulation technique is employed to analyze the crack region structure and extract the density features. As regarding skeletonized crack sections rather than crack pixels, the spatial distribution features hold considerable feature significance for each type of cracks. Empirical study indicates a classification precision of over 98% of the proposed approach.

https://link.springer.com/content/pdf/10.1155%2F2011%2F649675.pdf

Pavement Crack Classification via Spatial Distribution Features

The increasing popularity of global positioning systems (GPSs) has prompted transportation researchers to develop methods that can automatically extract and classify episodes from GPS data. This paper presents a transferable and efficient method of extracting and classifying activity episodes from GPS data, without additional information. The proposed method, developed using Python®, introduces the use of the multinomial logit (MNL) model in classifying extracted episodes into different types: stop, car, walk, bus, and other (travel) episodes. The proposed method is demonstrated using a GPS dataset from the Space-Time Activity Research project in Halifax, Canada. The GPS data consisted of 5127 person-days (about 47 million points). With input requirements directly derived from GPS data and the efficiency provided by the MNL model, the proposed method looks promising as a transferable and efficient method of extracting activity and travel episodes from GPS data.

Making mode detection transferable: extracting activity and travel episodes from GPS data using the multinomial logit model and Python

........................................................................................................................ iii Acknowledgements...................................................................................................... v List of Figures............................................................................................................... xii List of Tables................................................................................................................ xiv List of Abbreviations.................................................................................................... xv Preface.......................................................................................................................... xvii Chapter

/pdf/gis-based-episode-reconstruction-using-gps-data-for-activity-29nd7n4yl1.pdf

GIS-based Episode Reconstruction Using GPS Data for Activity Analysis and Route Choice Modeling

Methods for automated classification of pavement distress images are examined and compared. Images are divided into regions, and a two-stage classification procedure takes place. First, the regions are classified into primitives (plain, longitudinal, transverse, diagonal, or joint), which are the building blocks characterizing the various distress classes. The regional classification results are aggregated and used as input for the classification of the entire image to one of the classes of interest (plain, longitudinal, transverse, block, or alligator). A large number of features are examined using discriminant analysis, k-Nearest Neighbor, and discrete choice models. Conclusions are drawn on the discriminatory power of the various features and the appropriateness of the classification methods. For both levels, the logit model provided the best and most robust classification results. The sensitivity of the overall classification accuracy to the accuracy of primitive classification is also investigated.

Vassilios I. Kapotis

Papers

Improved methods for classification of pavement distress images