No abstract

In this study, the application of four classification techniques for computer vision–based pavement crack detection systems was investigated. The classification methods—artificial neural network (ANN), decision tree, k–nearest neighbor, and adaptive neuro-fuzzy inference system (ANFIS)—were selected on the basis of the complexity and clarity of their procedures. These methods were evaluated for ( a) prediction performance, ( b) computation time, ( c) stability of results for highly imbalanced data sets, ( d) stability of the classifiers’ performance for pavements in different deterioration stages, and ( e) interpretability of results and clarity of the procedure. According to the results, the ANN and ANFIS methods not only provide superior performance but also are more flexible and compatible for the crack detection application. The ANFIS method is called a “white-box classifier,” and the inferred knowledge from its membership functions can be used to characterize the imagery properties of detected image components.

MorphLink-C is a novel image-processing algorithm to connect crack fragments that are a common problem in crack recognition applications. The algorithm consists of two subprocesses: ( a) the grouping of fragments by using a morphological dilation transform and ( b) the connection of fragments by using a morphological thinning transform. MorphLink-C can be used with various crack extraction methods to connect crack fragments in crack line paths and for complicated crack shapes, such as single cracks, branched cracks, block cracks, and alligator cracks. MorphLink-C also provides a simple but accurate way to estimate an averaged crack width that is important in measuring cracking severity. The proposed method was validated by using realistic road surface images in different pavement cracking conditions. The results of the statistical hypothesis test showed that the proposed method could improve crack detection accuracy with the proposed crack defragmentation algorithm.

This paper presents the bridge cable inspection robot developed in Korea. Two types of the cable inspection robots were developed for cable-suspension bridges and cable-stayed bridge. The design of the robot system and performance of the NDT techniques associated with the cable inspection robot are discussed. A review on recent advances in emerging robot-based inspection technologies for bridge cables and current bridge cable inspection methods is also presented.

No abstract

No abstract