Fully convolution network architecture for steel-beam crack detection in fast-stitching images

Abstract

Crack detection using image processing has recently become a major research topic in nondestructive inspection (NDT) and structural health monitoring (SHM). However, crack detection methods are not robust to variations such as illumination, weather, and noise, due to the speed and accuracy of stitching cannot meet the requirements of practical applications. Therefore, an automated crack detection system built basing on deep learning to replace manual visual inspection. This article presents a SIFT matching method based on an alternate-selection strategy to eliminate residual reprojection and geometric distortion errors. A variety of network models based on the fully convolutional network (FCN) architecture are proposed to address the problems of local information losses and partial refinement capacity reductions for improving detection accuracy and reduce the error mark under a complex background, which are frequently encountered in the crack detection algorithms of deep learning methods. Combining pretraining with fine-tuning to verify the detection performance of FCN-based networks and use the feature descriptor to determine the location and size of each crack. Verifing the robustness of the proposed approach for the steel-beam crack detection task on the crack image dataset by an extensive experimental evaluation.