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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.
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.
At head of title: National Cooperative Highway Research Program.
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.