A fully integrated system for the automatic detection and characterization of cracks in road flexible pavement surfaces, which does not require manually labeled samples, is proposed to minimize the human subjectivity resulting from traditional visual surveys. The first task addressed, i.e., crack detection, is based on a learning from samples paradigm, where a subset of the available image database is automatically selected and used for unsupervised training of the system. The system classifies nonoverlapping image blocks as either containing crack pixels or not. The second task deals with crack type characterization, for which another classification system is constructed, to characterize the detected cracks' connect components. Cracks are labeled according to the types defined in the Portuguese Distress Catalog, with each different crack present in a given image receiving the appropriate label. Moreover, a novel methodology for the assignment of crack severity levels is introduced, computing an estimate for the width of each detected crack. Experimental crack detection and characterization results are presented based on images captured during a visual road pavement surface survey over Portuguese roads, with promising results. This is shown by the quantitative evaluation methodology introduced for the evaluation of this type of system, including a comparison with human experts' manual labeling results.

Automatic Road Crack Detection and Characterization

In the field of noninvasive sensing techniques for civil infrastructures monitoring, this paper addresses the problem of crack detection, in the surface of the French national roads, by automatic analysis of optical images. The first contribution is a state of the art of the image-processing tools applied to civil engineering. The second contribution is about fine-defect detection in pavement surface. The approach is based on a multi-scale extraction and a Markovian segmentation. Third, an evaluation and comparison protocol which has been designed for evaluating this difficult task—the road pavement crack detection—is introduced. Finally, the proposed method is validated, analysed, and compared to a detection approach based on morphological tools.

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Automatic Road Pavement Assessment with Image Processing: Review and Comparison

Human observation is commonly used to collect pavement surface distress data, during periodic road surveys. This method is labour-intensive, subjective and potentially hazardous for both inspectors and road users. This paper presents a novel framework for automatic crack detection and classification using survey images acquired at high driving speeds. The resulting images are pre-processed using morphological filters for reducing pixel intensity variance. Then, a dynamic thresholding is applied to identify dark pixels in images, as these correspond to potential crack pixels. Thresholded images are divided into non-overlapping blocks for entropy computation. A second dynamic thresholding is applied to the resulting entropy blocks matrix, used as the basis for identification of image blocks containing crack pixels. The classification system then labels images as containing horizontal, vertical, miscellaneous or no cracks. Two image databases are used for test purposes, to infer about the method's robustness, one of which acquired using professional high speed equipment.

Automatic road crack segmentation using entropy and image dynamic thresholding

Two optimized architectures of Context-based Adaptive Binary Arithmetic Coding (CABAC) are presented in the paper. These are: the software version of CABAC (dedicated to IA-32 and DSP platforms) and the hardware version of CABAC (dedicated to FPGA and ASIC platforms). The paper presents analysis of implementations for both versions of CABAC. The optimized software as well as the hardware version of CABAC was tested in terms of codec throughput.

A Novel LBP Based Methods for Pavement Crack Detection

Pavement distress detection and analysis is the most important part of automated pavement inspection systems. Due to the circumstances such as complex texture, uneven illumination, and nonuniform background, pavement distress detection is not a simply edge detection process. Over the past 30 years, lots of methods were proposed to detect pavement distresses, especially cracks. In this letter, a novel automatic pavement crack detection approach based on texture analysis and shape descriptors is proposed. Pavement surface is seen as a texture surface, and distresses are defined as inhomogeneities occurring in the texture surface. Six texture features and two translation-invariant shape descriptors were used here as discriminate features against irregular texture and uneven illumination. By using a SVM classifier, all sub-images are classified as crack or non-crack. Final results were obtained after post-processing, which includes segmentation, fake-crack eliminating, and crack-measuring methods. Com...

Automatic Pavement Crack Detection Using Texture and Shape Descriptors

Automation of pavement surface distress survey is of considerable interest since it facilitates road maintenance. Pavement distress detection algorithm is proposed, which includes preprocessing and artificial life algorithm. In nature, bee population consists of different species of bees. Different species of bees cooperate with others and build combs. The process of building combs is based on a bottom-up structure instead of a top-down centralized controller like expert system and exhibits the emergent properties of bee population. Artificial living system in artificial life algorithm is very similar to bee population. In artificial life algorithm, artificial living system is an artificial population, which is composed of different species of artificial organisms. Artificial living system is based on a bottom-up synthetic approach and exhibits its emergent properties for distress detection. Experimental results demonstrate the proposed method has strong effect on noise removal, oilstains elimination and the rid of dark spots.

Q. Wang

Papers

Use of artificial living system for pavement distress survey