Analysis of Existing Research on Crack Detection Using Image Processing, Deep Learning, and Machine Learning

Crack detection plays a vital role in ensuring the structural integrity of various infrastructures, including roads, bridges, and pipelines. Manual inspection methods are time-consuming, labor-intensive, and prone to error. Recent advances in image processing, machine learning (ML), and deep learning (DL) have facilitated the development of automated systems that can efficiently detect cracks with high precision. This paper presents an extensive review of the state-of-the-art methods used for crack detection through these technologies, highlighting their strengths, limitations, and future research directions. Crack detection is an important task in many fields, such as infrastructure inspection and maintenance. Cracks can indicate structural damage and pose safety hazards. Automating crack detection using image processing techniques has gained popularity due to its speed and cost-effectiveness compared to manual inspection methods (Bhat et al., 2020). Traditional methods often rely on manual feature engineering, which can be time-consuming and may not generalize well to different crack types and backgrounds. However, recent advances in deep learning, particularly convolutional neural networks, have shown promising results in automating crack detection (Fei et al., 2023). CNNs can automatically learn hierarchical features from images, making them suitable for detecting cracks with varying shapes, sizes, and textures. Despite the progress, challenges remain in crack detection, such as accurately detecting thin cracks with sub-pixel widths (Pushing the Envelope of Thin Crack Detection, 2021), handling intensity inhomogeneity, and distinguishing cracks from noise and other background clutter (CrackFormer: Transformer Network for Fine-Grained Crack Detection, 2021). Researchers are actively developing more robust and accurate crack detection algorithms using advanced deep learning architectures like Transformers (CrackFormer: Transformer Network for Fine-Grained Crack Detection, 2021) to address these challenges.