A Neural Network-Based Asphalt Pavement Crack Classification Model Using Image Processing and Random Boosted Differential Flower Pollination.

This study proposes a novel approach for detecting and recognizing asphalt pavement cracks. Image processing techniques including discrete Fourier transform (DFT), steerable filter (SF), and integral projection (IP) are combined to extract features from pavement images. The new method employs DFT as a low-pass filter to enhance the original digital image. Accordingly, a crack salient map is constructed by the utilization of SF. Based on such salient map, IP of the image is established and its statistical properties are computed to characterize the condition of a pavement image. A soft computing model that integrates the artificial neural network, Levenberg–Marquardt (LM) backpropagation training, and Differential Flower Pollination (DFP) algorithm is put forward for the task of pavement recognition. The integration of LM and DFP aims at combining the global search capability of metaheuristic and the local search efficiency of the steepest descent-based backpropagation approach. During the optimization process of DFP, LM algorithm is performed with a randomly selected member of the population to boost the convergence rate and enhance the searching effectiveness. This new training framework of ANN is, therefore, named as ANN with Random Boosted Differential Flower Pollination (RBDFP-ANN). A data set of pavement images including four class labels (no crack, longitudinal crack, transverse crack, and alligator crack) has been collected to train and verify the RBDFP-ANN. Experimental results supported by statistical test confirm that the proposed model achieves a superior prediction accuracy rate (89.25%) over other benchmark methods. Accordingly, the RBDFP-ANN model is very potential to be used by transportation agencies in the task of asphalt pavement inspection. [ABSTRACT FROM AUTHOR]