Crack segmentation in high-resolution images using cascaded deep convolutional neural networks and Bayesian data fusion

Wen Tang , Rih-Teng Wu , Mohammad R. Jahanshahi

Abstract

Manual inspection of steel box girders on long span bridges is time-consuming and labor-intensive. The quality of inspection relies on the subjective judgements of the inspectors. This study proposes an automated approach to detect and segment cracks in high-resolution images. An end-to-end cascaded framework is proposed to first detect the existence of cracks using a deep convolutional neural network (CNN) and then segment the crack using a modified U-Net encoder-decoder architecture. A Naive Bayes data fusion scheme is proposed to reduce the false positives and false negatives effectively. To generate the binary crack mask, first, the original images are divided into 448 × 448 overlapping image patches where these image patches are classified as cracks versus non-cracks using a deep CNN. Next, a modified U-Net is trained from scratch using only the crack patches for segmentation. A customized loss function that consists of binary cross entropy loss and the Dice loss is introduced to enhance the segmentation performance. Additionally, a Naive Bayes fusion strategy is employed to integrate the crack score maps from different overlapping crack patches and to decide whether a pixel is crack or not. Comprehensive experiments have demonstrated that the proposed approach achieves an 81.71% mean intersection over union (mIoU) score across 5 different training/test splits, which is 7.29% higher than the baseline reference implemented with the original U-Net.

Key Words

Bayesian data fusion; crack detection; deep learning; semantic segmentation; structural health monitoring

Address

(1) Wen Tang, Mohammad R. Jahanshahi — Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47906, USA
(2) Mohammad R. Jahanshahi — Elmore Family School of Electrical and Computer Engineering (Courtesy), Purdue University, West Lafayette, IN 47907, USA
(3) Rih-Teng Wu — Department of Civil Engineering, National Taiwan University, Taipei, Taiwan.

PDF Viewer

Preview is limited to the first 3 pages. Sign in to access the full PDF.

← Back to Volume 29, No. 1