Precisely identifying pavement cracks from charge-coupled devices (CCDs) captured high-
resolution images faces many challenges. Even though convolutional neural networks …

The use of deep learning (DL) in civil inspection, especially in crack detection, has
increased over the past years to ensure long-term structural safety and integrity. To achieve …

Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. As a result,
designing an automated detection system for citrus plant diseases is important. Deep …

Crack detection is an indispensable premise of road maintenance, which can provide early
warning information for many road damages and save repair costs to a large extent …

Numerous detection problems in computer vision, including road crack detection, suffer from
exceedingly foreground–background imbalance. Fortunately, modification of loss function …

“Road detection is said to be a major research area in remote sensing analysis and it is
usually complex due to the data complexities as it gets varied in appearance with minor inter …

The dataset is crucial for the results of crack segmentation in deep learning. However, the
quantity and quality of annotations in datasets used for crack segmentation are uneven, and …

The understanding of roads and lanes incorporates identifying the level of the road, the
position and count of lanes, and ending, splitting, and merging roads and lanes in highway …

In this research, an attention-based feature fusion network (AFFNet), with a backbone
residual network (ResNet101) enhanced with two attention mechanism modules, is …

Pixel-level crack extraction (PCE) is challenging due to topology complexity, irregular edges,
low contrast ratio, and complex background. Recently, Transformer architectures have …