CNN‐based multi‐output regression model to estimate infrastructural surface crack dimensions adopting a generalised patch size and FWHM‐based width quantification

Abstract To cater the need for real‐time crack monitoring of infrastructural facilities, a CNN‐regression model is proposed to directly estimate the crack properties from patches. RGB crack images and their corresponding masks obtained from a public dataset are cropped into patches of 256 square pixels that are classified with a pre‐trained deep convolution neural network, the true positives are segmented, and crack properties are extracted using two different methods. The first method is primarily based on active contour models and level‐set segmentation and the second method consists of the domain adaptation of a mathematical morphology‐based method known as FILFINDER. A statistical test has been performed for the comparison of the stated methods and a database prepared with the more suitable method. An advanced convolution neural network‐based multi‐output regression model has been proposed which was trained with the prepared database and validated with the held‐out dataset for the prediction of crack‐length, crack‐width, and width‐uncertainty directly from input image patches. The proposed model has been tested on crack patches collected from different locations. Huber loss has been used to ensure the robustness of the proposed model selected from a set of 288 different variations of it. Additionally, an ablation study has been conducted on the top 3 models that demonstrated the influence of each network component on the prediction results. Finally, the best performing model HHc‐X among the top 3 has been proposed that predicted crack properties which are in close agreement to the ground truths in the test data.