Crack Detection

Crack detection, crucial for infrastructure maintenance and safety, aims to automatically identify and segment cracks in images of various surfaces (roads, buildings, bridges). Current research heavily utilizes deep learning, focusing on convolutional neural networks (CNNs), transformers, and hybrid architectures like encoder-decoder models, often incorporating attention mechanisms and novel loss functions to improve accuracy and efficiency, particularly in challenging conditions like low light or noisy backgrounds. These advancements enable faster, more reliable assessments of structural integrity, reducing manual inspection costs and improving safety by facilitating timely repairs. The field is also actively developing larger, more diverse datasets and exploring unsupervised and weakly-supervised learning techniques to address data limitations.