Crack recognition on concrete structures based on machine crafted and hand crafted features

Abstract

Concrete structures play a critical role in infrastructure development, and their safety is of utmost importance. Cracks in concrete structures can be a sign of deterioration, which may affect the overall safety of the building. Regular inspections and monitoring of surface cracks are essential to ensure the structural integrity of the building. However, human inspection can be time-consuming and subjective, leading to inconsistent findings. Hence, computer vision based crack recognition is necessary. In this work, we propose three custom designed deep networks and a multi resolution framework, named, Quaternionic wavelet transform (QWT) for crack recognition. The efficiency of deep network (machine crafted features) and QWT (hand crafted features) is studied in detail on three datasets namely SDNET2018, METU (Middle East Technical University) concrete crack image dataset and Historical-crack18-19 dataset. Also, the effect of wavelet scale on crack recognition and the implication of the fully connected layers in deep convolutional neural networks are studied in this research work. Finally, we infer that QWT features achieve a maximum accuracy of 98.44%, 99.80% and 94.67% respectively on the above said datasets, and their performance is comparable to that of machine-crafted (deep) features and with the other state of art methods.