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Videotapes of highway pavement surfaces are collected with the Automatic Road Analyzer (ARAN). The videotapes are then brought back to the lab for visual evaluation. Therefore, there exists a need to automatically determine the type and extent of cracking by computerized means. The report describes the image analysis algorithm to classify the cracking of Asphalt-concrete Pavement (ACP) and Continuously Reinforced Concrete Pavement (CRCP). The image analysis software features a three-pass approach. The first pass detects crack segments from the analysis of the block projection histogram. The second pass re-examines the vicinity of the detected edge segments to locate the remaining thinner crack segments with less stringent rules. The third pass is to classify the cracking type based on the position alignment and orientation of the crack segments in the edge map.
This book constitutes Part III of the refereed four-volume post-conference proceedings of the 4th IFIP TC 12 International Conference on Computer and Computing Technologies in Agriculture, CCTA 2010, held in Nanchang, China, in October 2010. The 352 revised papers presented were carefully selected from numerous submissions. They cover a wide range of interesting theories and applications of information technology in agriculture, including simulation models and decision-support systems for agricultural production, agricultural product quality testing, traceability and e-commerce technology, the application of information and communication technology in agriculture, and universal information service technology and service systems development in rural areas.
The evaluation of pavement condition is an important part of pavement management. To evaluate a pavement, a distress survey has been performed mainly by manual field inspections. Several automatic pavement evaluation systems have been developed to overcome the drawbacks of field inspections. Automated evaluation systems, however, imply their own limitations in terms of cost, technical problems, and adaptability for pavement management. The main purpose of this research is to develop a low-cost automatic pavement video imaging system. The secondary purpose is the development of techniques to process the collected video images. A low-cost video image-collection system and an in-office system were developed. A video test was implemented on a selected route including various pavement types and several variables. As a result of the test, seven loop tests provided acceptable results to allow image analysis. By using the video camera with fast shutter-speed, it was decided that the survey vehicle could drive at high speed (65mph) while maintaining good picture quality. To evaluate the performance of the system, video and field inspections were performed using two approaches: the Oregon Department of Transportation (ODOT) and Metropolitan Transportation Commissions (MTC) approaches. The inspections were conducted on 107 sample sections. Also, sample still images were digitized for analysis. To conduct a video inspection, the Global Positioning System (GPS) technique was applied for conversion of video mileage to real field mileage. The results of video and field inspections were compared using statistical analyses. The ODOT approach shows a good correlation between video and field inspection for AC sections. In particular, patching and non-load crack indices provide good correlation. The MTC-PMS analysis showed strong linear relationships between video and field inspections. The analysis of crack indices from digitized images shows poor repeatability for each test loop. Using general linear model analysis, variable effects on crack indices were tested. The cost for development and operation of the system was estimated as well as cost for an enhanced prototype system. Discussions on various aspects of the developed system are provided. Finally, summary and conclusion are included as well as recommendations for future system development.
At head of title: National Cooperative Highway Research Program.
Over the years, Automated Image Analysis Systems (AIAS) have been developed for pavement surface analysis and management. The cameras used by most of the AIAS are based on Charge-Coupled Device (CCD) image sensors where a visible ray is projected. However, the quality of the images captured by the CCD cameras was limited by the inconsistent illumination and shadows caused by sunlight. To enhance the CCD image quality, a high-power artificial lighting system has been used, which requires a complicated lighting system and a significant power source. In this thesis, we will first introduce a high-efficiency and economical approach for pavement distress inspection by using laser imaging. After the pavement images are captured, regions corresponding to potholes are represented by a matrix of square tiles and the estimated shape of the pothole is determined. The vertical, horizontal distress measure, the total number of distress tiles and the depth index information are calculated and input into a three-layer feed-forward neural network for pothole severity and crack type classification. We also introduced an adaptive pavement distress segmentation method based on Genetic Algorithm, which can dynamically locate the optical threshold in the search space. The proposed analysis algorithms are capable of enhancing the pavement image, extracting the pothole from background and analyzing its severity. To validate the system, actual pavement pictures were taken from pavements both in highway and local roads. The experimental results demonstrated that the proposed model works well for pothole and crack detection.
The rational allocation of pavement maintenance resources requires the periodic assessment of the condition of all pavements. Traditional manual pavement distress surveys, which are based on visual inspection, are labor intensive, slow, and expensive, and they pose a safety hazard when the raters have to get out of their vehicles and inspect the road on foot. One of the principal goals of this report is to provide the Virginia Department of Transportation with some of the important background required for determining whether, with the current state of development of image processing and camera and computer hardware, it is feasible to develop an automated system to use for pavement distress surveys. This report describes some of the fundamental techniques of image processing that are likely to play a role in a pavement survey system that can automatically recognize and classify cracks. The report concludes that developments in technology during the last 10 to 15 years have made it possible to develop and implement such a system and that the implementation of such a system would mean that surveys would be less expensive, faster, safer, and more objective.