Employing histogram of oriented gradient to enhance concrete crack detection performance with classification algorithm and Bayesian optimization

This paper develops a crack detection method for concrete bridges using a binary classification algorithm, which utilizes the histogram of oriented gradient (HOG) feature to effectively and efficiently capture the crack characteristic. The method is implemented in three steps: collecting images with and without cracks, HOG feature calculation as input variables, and crack detection with a binary classification algorithm. Moreover, the influences of different HOG parameters are investigated to better capture the cracks' characteristics. The classification algorithm is adopted as a support vector machine (SVM) model, and Bayesian optimization is employed to select hyperparameters in the model training. The method is verified with available datasets of images with and without cracks from concrete bridge decks. The corresponding performance is compared to that of other methods, such as convolutional neural network (CNN), Adaboost, K-Nearest Neighbor (KNN), and Naive Bayes, evidencing higher accuracy and computation efficiency.