In this code I use many image processing and image segmentation techniques to detect cracks in pavements images using Matlab. Also the code uses an estimation of the area in image to estimate the dimensions of the cracks in meters.
Abstract:This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.Keywords: road distress detection; road surface classification; linear features; multi-class SVM; local binary pattern; gray-level co-occurrence matrix
Periodic road inspections in order to have accurate and up-to-date information about the road surface condition are the most efficient way to maintain high road standards at the lowest price. Distress measurement is a crucial factor when evaluating road quality, being cracking the best indicator of the need of preventive maintenance treatments. The type, length and severity of cracks are used to quantify the road condition and identify the source of deterioration. Cracking appears in the first stages of worsening, so its detection will allow one to perform a proper maintenance, saving huge amounts of money on a later restoration. In addition, road deterioration is quite gradual in most of the road surfaces as described in . Road surfaces deteriorate only 40 percent in quality during the first 75 percent of their life. Then, if not treated, the slope becomes significantly steeper as a consequence of water penetration and continued loading, and another 40 percent decrease in quality is produced in the next 12 percent of life . Roads with incipient deterioration can be identified through road management programs since preventive maintenance can be applied following a cost-effective strategy (reducing the cost by a factor of 5).
Depending on the degree of human intervention required, distress detection methods can be categorized in purely manual, semi-automated, and automated. Manual surveys have been used for long time and despite the fact that automated methods are becoming more common, they are still the most frequent methodology . Human inspections present several problems, including those related to the lack of consistency among operator criteria. A great economic effort has been made by authorities and road owners to overcome the difficulties found in the developing of automated systems. Many researchers have worked on this problem, developing first semi-automatic systems to reach later fully automated ones. Semiautomatic systems use different collection technologies to grab road images and postpone the distress identification to an off-line process running in workstations, improving the safety but using still manual distress detection, or at least an important level of intervention. The identification of various distress types, as well as their severity and extent from images requires observers who have been well trained in both pavement distress evaluation and in the use of the workstations. Therefore, it is necessary to add the cost of qualified staff to the cost of expensive collection devices, discouraging agencies from adopting these technologies .
As an example of laser-based system we note the GIE LaserVISION system . This system uses four laser sensors and provides 3D measurements giving the system the potential for improved distress measurement. Nonetheless, it has too low resolution, 3 mm by 110 mm, so it is limited to measuring transverse cracking. The most promising innovative inspection methods are evaluated in . A newly developed survey system, Mobile 3D Video Mapping (M3DVM), based on a set of 360° laser scanners positioned on a van is presented. This technique needs still further development as the operation costs are too high and the location error of points in the imagery is about 5 cm. Further development is also recommended for another approach, Car as Sensor, which uses a fleet of cars equipped of sensors to collect data about road conditions. Other two systems, unmanned aerial vehicles and spaceborne techniques, are considered inappropriate for road distress detection since they present low resolution images and low flexibility sensors.
Comparing the different commercial systems is not always feasible. The understandable competitive spirit of the different manufacturers makes the information shared about their system weaknesses and the true performance minimal. Detection performance has a high dependency on the set of roads assessed. The road condition itself, the presence of non-crack elements and the different texture backgrounds faced in each case will be decisive. However, there is no public dataset with sequences of road images available so it is not possible to carry out a proper comparison. In addition, systems present different survey width making it harder to compare. In  it is noticed that the lack of standardized methods for evaluating the precision and repeatability of the systems constitutes a problem. Despite the harmonization efforts undertaken, different protocols for cracking evaluation are still used. Finally, the variety of levels of human intervention actually needed by the automatic systems also makes the comparison more complex. In general, commercial systems present problems identifying and quantifying cracking according to a protocol, especially those thinner than 3 mm, reaching an acceptable performance only when considering network-level tests. An investigation undertaken by the TRL [17,18] to assess the performance of five commercially available crack identification systems, including Fugro, Waylink and TRL systems, concluded that all the evaluated systems had problems with common non-crack features present on the road surface, including joints, patches, road marking(s) and road edges, resulting in much more cracking being reported than was in the reference data. Moreover, the accuracy was inconsistent, varying the performance from location to location as a consequence of the different types of road surfaces surveyed. All the systems would have difficulties in reaching the TRL requirements for acceptance, only approaching these requirements when manual intervention is used as part of the identification process. In 2008 the American Transport Research Bureau presented the 2nd Strategic Highway Research Program results , which shows that manual interpretation systems are widely used and automated systems are still under development in various Departments of Transport and in private companies. They consider that manual intervention should be eliminated in order to reduce cost and pointed out some limitations of current automated systems. Fatigue cracking and sealed cracks are difficult to quantify and lighting conditions and shadows cause problems. According to this report the barriers to progress are the need for better lighting systems and a better interpretation software.
This paper presents a road distress detection system that consists of an on-line images recording process and an off-line images processing stage. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. The processing module includes the phases needed to properly deal with fully automatic road distress assessment. The texture is smoothed and the linear features are enhanced by means of a pre-processing stage. Non-crack features such as joints, sealed cracks and white painting, that are usually mistakenly reported as crack features, are specifically detected and masked. A seed-based approach is then used to detect road cracks, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. The whole detection process involves the use of several parameters that have to be adapted depending on the type of pavement to get optimal results without manual intervention. A linear SVM-based classifier ensemble trained to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes a combination of different texture-based features. The system parameters are then tuned depending on the output provided by the classifier.
The remainder of this paper is organized as follows. In Section 3 the overview of the whole road crack detection system is described in brief. Section 4 provides the description of the different system stages. Experimental results are presented in Section 5, and Section 6 concludes this paper and refers to future extensions of this research. 2b1af7f3a8