Convolutional neural networks (CNNs) have gained growing interest in recent years for their
advantages in detecting cracks on concrete bridge components. Class imbalance is a …

Cracks are the most common damage type on the pavement surface. Usually, pavement
cracks, especially small cracks, are difficult to be accurately identified due to background …

Despite recent breakthroughs in deep learning and computer vision fields, the pixel-wise
identification of tiny objects in high-resolution images with complex disturbances remains …

Crack inspection in railway sleepers is crucial for ensuring rail safety and avoiding deadly
accidents. Traditional methods for detecting cracks on railway sleepers are very time …

Automatic crack segmentation plays an important and challenging role in pavement
maintenance. In recent years, researchers have been trying to figure out the task that long …

One of the most significant problems affecting a concrete bridge's safety is cracks. However,
detecting concrete bridge cracks is still challenging due to their slender nature, low contrast …

Damage assessment of concrete structures is necessary to prevent disasters and ensure the
safety of infrastructure such as buildings, sidewalks, dams, and bridges. Cracks are among …

Automatic detection of pavement cracks is an important task to ensure pavement safety.
However, noise and uneven cracks in real pavement images present great challenges for …

Earthen heritage sites are historical relics left by ancient human activity, with earthen as the
primary building material, and have significant historical, scientific, and artistic value …

Utilizing convolutional neural network (CNN) models, computer vision technology has
become a reliable and powerful tool for detecting potential damage in concrete structures at …