A crack detection and quantification framework for high-resolution images using Mamba and unmanned devices

Abstract

In structural defects inspection, the quantitative detection of slender cracks remains a significant challenge. Existing methods suffer from low segmentation accuracy for complex boundaries and high computational demands for high-resolution (HR) images, making them unsuitable for the current scenarios where unmanned devices are widely deployed. To address the above-mentioned limitations, a crack detection and quantification framework based on multi-scale convolution-enhanced Mamba (MCMamba) and an HR image calibration method is proposed. The MCMamba is designed based on the Mamba architecture and the calibration method using variable step-size moving least squares is proposed to fit the scale field of HR images, enabling precise crack segmentation and quantification. The MCMamba is trained on an established dataset, and the framework is further field-tested using a climbing robot and Unmanned Aerial Vehicle (UAV), achieving accuracy with less than 10% error for cracks thinner than 0.2 mm. This framework improves crack detection accuracy and demonstrates its advantages in quantifying slender cracks on large-scale bridges in engineering practice.