A Bidirectional Self-Rectifying Network With Bayesian Modeling for Vision-Based Crack Detection

Robotic vision is increasingly applied for surface inspection of built infrastructure. For this, it is essential to develop robust algorithms for semantic segmentation. This article presents a deep learning approach using a bidirectional self-rectifying network with Bayesian modeling (BSNBM) for improving detection accuracy, in dealing with the embedded uncertainty caused by false-positive labels and nonlinearity in sequentially convolutional blocks. For integration with residual encoders, a feature preserving branch is designed, wherein the output of previous dilated convolutional blocks is upsizedly or downsizedly passed on and concatenated with the following blocks recursively and bidirectionally. Further, to achieve robustness in feature representation with an acceptable level of credibility, convolutional kernels are randomized via a Bayesian model and adjusted per evidence update. As such, the network becomes less sensitive to uncertainty and redundant nonlinearity, which is inevitable in activation layers. Experimental results confirm the advantage of our BSNBM over current crack detection approaches.

I. Introduction

Surface crack is a structural health indicator for the defect in transportation and civil assets such as roads, bridges, and buildings. Their effective maintenance and evacuation plans in residential zones for safety rest with the prompt inspection to such signs of abnormal cracks. For dangerous and unattainable inspection sites, it is wise to conduct robotic monitoring on cracks using e.g., unmanned aerial vehicles (UAVs) and unmanned ground vehicles. Vision-based crack detection requires a strong technique to address the important issue of similar binary segmentation [1] from the extraction of generic features. With advances in neural computing, deep convolutional neural networks (DCNNs) have emerged as a powerful tool that can effectively address this requirement in order to improve robustness and accuracy by learning from data.