Crack Segmentation

Crack segmentation, the automated identification and delineation of cracks in images, aims to improve infrastructure inspection and maintenance by providing efficient and accurate damage assessment. Current research focuses on developing robust and computationally efficient deep learning models, employing architectures such as convolutional neural networks (CNNs), transformers, and hybrid approaches that leverage both local and global feature extraction, often incorporating attention mechanisms and novel loss functions to address challenges like class imbalance and noisy backgrounds. These advancements are crucial for automating visual inspections of bridges, roads, and other structures, leading to improved safety, reduced maintenance costs, and more effective structural health monitoring.