Asphalt Pavement Crack Detection Based on Fusion of Neighborhood and Gradient Salient Features

Abstract

In order to solve the problem of the rich texture noise and serious misidentification of small cracks in the asphalt pavement crack detection,, an asphalt pavement crack detection method that combines neighborhood and gradient salient features is proposed. Firstly, the grayscale correction and morphological reconstruction are used to reduce the influence of external interference and uneven brightness caused by the bright spot noise in the rich texture, then the neighborhood salient features are extracted according to the salient differences between pixels and their neighborhood, and gradient salient features in different directions are obtained through the steerable filter. With the convolutional fusion of the neighborhood and gradient salient features, preferential directions are chosen to generate the salient map of feature fusion. Secondly, the suspected crack aggregation areas are obtained by the threshold segmentation of salient map. Based on the different geometric characteristics of the aggregation areas, a clustering analysis method is introduced to select the crack candidate area. Finally, the method of searching and locating a regional endpoint is proposed to eliminate the subsets without endpoint aggregation area, the endpoints of different regions are connected to achieve the complete crack extraction. The experimental results of the collected asphalt pavement crack image datasets show that the precision, recall and <italic>F</italic>-measure value are 92.857%, 86.405% and 89.515%, which can effectively detect cracks in asphalt pavement images, especially for small cracks, and provide more accurate crack information for pavement maintenance.