A Pavement Crack Registration and Change Identification Method Based on Unsupervised Deep Neural Network

Zhengfang Wang, Hongliang Zhu,Yujie Yang,Haonan Jiang, Wenhao Li, Bingrui Li, Peng Li, Lei Xu,Qingmei Sui,Jing Wang

IEEE Transactions on Intelligent Transportation Systems（2025）CCF BSCI 1区SCI 2区

Shandong Univ | Qilu Aerosp Informat Res Inst | Changjiang Geophys Explorat & Testing Co Ltd

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Abstract

Periodically monitoring the pavement cracks is of great importance to many transportation infrastructures. This paper proposed an unsupervised deep-learning-based method to match the cracks in multi-temporal unmanned aerial vehicle (UAV) images and identify the changes of pavement cracks over time. A regional focus module was specially designed to enforce the network to focus on regions where cracks were located and enhance its capacity for small-crack identification. Moreover, a data augmentation method which combined Poisson blending and random projective transformations was introduced for generating images with crack variations for model training. The superiority of the method was validated using actual image collected from real pavements. The experimental results showed that the proposed method outperformed the feature-based method and existing unsupervised deep learning-based UAV image registration method.

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Key words

Feature extraction,Autonomous aerial vehicles,Image registration,Inspection,Convolutional neural networks,Remote sensing,Image segmentation,Biomedical imaging,Artificial neural networks,Surface cracks,Deep neural network,pavement inspection,crack variation identification,multi-temporal UAV images

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