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• An automatic method for pavement patch detection is proposed. • Image processing is used to compute pavement image texture. • Least Squares Support Vector Machine is used for data classification. • Differential Flower Pollination is employed for model optimization. • The new model can achieve a good prediction result (CAR = 95%). Patch detection is an important task in pavement condition survey. This study establishes an automatic approach for asphalt pavement patch recognition based on image texture analysis and hybrid machine learning algorithms. Features based on image texture that employs statistical properties of color channels and the gray-scale co-occurrence matrix are used by the Least Squares Support Vector Machine (LSSVM) for discriminating patched areas from non-patch ones. In addition, to optimize the LSSVM training phase, the Differential Flower Pollination (DFP) metaheuristic is used. A data set constructed from a set of 1000 image samples has been utilized to train and verify the proposed integration of image texture analysis techniques, LSSVM, and DFP. Experimental results show that the new model can achieve a good prediction result with Classification Accuracy Rate = 95.30%, Positive Predictive Value = 0.96, and the Negative Predictive Value = 0.95. Additionally, a patch detection program has been developed and compiled in Visual C#.NET to ease the implementation of the hybrid model. Thus, the newly developed method can be a potential tool for traffic management agencies during the phase of pavement condition evaluation. [ABSTRACT FROM AUTHOR]

Fatigue crack initiation sites (FCISs) are crucial for fatigue investigations in metallic compounds, since they are closely related to the fatigue failure mechanisms and inform methods against crack initiation. However, the identification of FCISs requires professional knowledge and can be demanding and time consuming. Computer vision techniques show excellent performance for image texture recognition tasks and have been applied to some material domains. In order to explore more possibilities and widen the applications of computer vision techniques on material domains, as well as reduce the labor and knowledge requirements for FCISs recognition tasks, a state-of-the-art object detector – deeply supervised object detector which can train models from scratch, was introduced. The models trained from limited data show reasonable capacity for recognizing FCISs. Results demonstrate that increasing training dataset size can improve the accuracies of the models, while raising the number of epochs can result in a superior ability to recognize delicate features. Moreover, most of the images that cannot be recognized for FCISs possess common characteristics, such as, poor image quality, unclear features, insufficient training data and etc. Based on the above findings, several proposals are made for the future work to improve the performance of the same or relevant studies, as a reference. [ABSTRACT FROM AUTHOR]