Crack Detection Algorithm, We propose UDTIRI-Crack, Tunnel cracks are the main factors that cause damage and collapse of tunnel structures. While Moreover, the development of accurate crack detection model requires a significant amount of labelled training data to train robust ML algorithms. However, existing deep learning-based In this study, an improved target-detection model based on information theory is proposed to address the difficulties of crack-detection tasks, such as The detection of cracks is essential for assessing and maintaining building and road safety. However, the complexity of the crack background and the highly uneven pixel ratio between the crack and the background Crack detection is a critical task for bridge maintenance and management. Herein, a novel deep learning method based on convolutional neural networks, referred to as R-FPANet, is This section introduces DCNNs and their application for crack detection in detail. Crack images collected from civil infrastructures through unmanned aerial vehicles suffer from motion blur and insufficient resolution, which reduces the accuracy of microcrack detection. Herein, a novel real-time crack detection and The authors evaluated several DL-based crack identification algorithms from the literature, such as crack classification, crack object detection, pixel-level crack segmentation, generative Surface cracks in concrete structures can severely impact the safety and stability of buildings. We built a lightweight Afterwards, the existing challenges and future development trends of deep learning-based road crack detection algorithms are discussed. This study reviews This problem was resolved in [58] when the proposed crack detection and tracing algorithm extracts crack properties for crack detection. In this paper, the Crack detection for concrete pavement is an important and fundamental task to ensure road safety.
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