Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network

Crack detection is a crucial task in periodic pavement survey. This study establishes and compares the performance of two intelligent approaches for automatic recognition of pavement cracks. The first model relies on edge detection approaches of the Sobel and Canny algorithms. Since the implementation of the two edge detectors require the setting of threshold values, Differential Flower Pollination, as a metaheuristic, is employed to fine-tune the model parameters. The second model is constructed by the implementation of the Convolution Neural Network (CNN) - a deep learning algorithm. CNN has the advantage of performing the feature extraction and the prediction of crack/non-crack condition in an integrated and fully automated manner. Experimental results show that the model based on CNN achieves a good prediction performance of Classification Accuracy Rate (CAR) = 92.08%. This performance is significantly better than the method based on the edge detection algorithms (CAR = 79.99%). Accordingly, the proposed CNN based crack detection model is a promising alternative to support transportation agencies in the task of periodic pavement inspection.