Cracks identification using mask region-based denoised deformable convolutional network

Abstract

Cracks are one of the critical structural defects in building assessment to determine the integrity of civil structure. Structural surveying process using computer vision is required to automatically identify cracks. The application of Convolutional Neural Networks (CNNs) is limited by its fixed geometric kernels to extract the irregular shape of cracks. In this paper, a mask Region-based Denoised Deformable Convolutional Network (R-DDCN) is proposed to detect cracks for accurate instance segmentation and image classification. Denoised deformable convolution is introduced to improve the modeling capability of convolution layer. It adopts the existing deformable convolution, with non-local means as a denoising mechanism to optimize the augmentation of spatial sampling locations with filtered offsets. Experimental results show that the proposed mask R-DDCN has lower validation loss and improved mean accuracy precision of mAP75 from 66.7% to 76.7% as compared to the mask R-CNN. Mask R-DDCN can perform better modeling capability in cracks identification.