Search

This paper presents a computer vision framework supporting automated infrastructure damage detection, with a specific focus on surface crack detection in concrete. The approach presented is designed to provide a significant increase in robustness relative to existing methods when faced with widely varying field conditions while operating fast enough to be used in large scale applications. In particular, a clustering method for segmentation is developed that exploits inherent characteristics of fracture images to achieve consistent performance, combined with robust feature extraction to improve recognition algorithm classifier outcomes. The approach is shown to perform well in detecting cracks across a broad range of surface and lighting conditions, which can cause existing techniques to exhibit significant reductions in detection accuracy and/or detection speed. [ABSTRACT FROM AUTHOR]

Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible should be considered in high accuracy applications. Typical related works either focus on the performance of a given algorithm or compare various classification methods. In many occasions, however, researchers who are not experts in the field of machine learning have to deal with practical classification tasks without an in-depth knowledge about the underlying parameters. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. We carried out a performance study of nine well-known classifiers implemented in the Weka framework and compared the influence of the parameter configurations on the accuracy. The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM). In addition, the k-nearest neighbor method frequently allowed the best accuracy. In certain conditions, it was possible to improve the quality of SVM by more than 20% with respect to their default parameter configuration. [ABSTRACT FROM AUTHOR]