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Inspection of glass façade and concrete structures for damages such as cracks are crucial for buildings safety and maintenance. Such surface cracks result in appearance of objects of abnormal geometric shapes, an obvious motivation to investigate geometric descriptor features associated with visible objects on surfaces of such materials. Here, we are concerned with developing generic automatic vision-based inspection for detection and recognition of such infrastructure anomalies. This paper extends and enhances our earlier work on glass façade cracks. We propose and test the performance of several handcrafted texture feature descriptors to discriminate cracked surface material. These features include the Histogram of Oriented Gradients (HOG), the Uniform Local Binary Pattern (ULBP), together with quantised Linearity and curvature measures obtained post an edge detection procedure. Unlike the previous paper, we extract and concatenate these features from a 3x3 blocks that partition the input image. The performance of the proposed methods is tested on four datasets of glass cracks and a large dataset of concrete cracks. We shall demonstrate that the block-based approach yields significant (5%-10%) improvement compared to our earlier work on glass, and all features have high performance for concrete surfaces attaining 98.6% for HOG feature. Furthermore, we adapt CNN layers trained on the ImageNet dataset and transfer this knowledge to the crack surface recognition task. The significant efficiency of handcrafted features, compared to CNN models, raises issues on models suitability for implementation on board UAV and constrained mobile devices.
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