Deep learning techniques have largely solved the problem of rail surface defect detection
(SDD), however, two aspects have yet to be addressed. In most existing approaches, two …

The human visual system can rapidly focus on prominent objects in complex scenes,
significantly enhancing information processing efficiency. Salient object detection (SOD) …

Owing to the development of convolutional neural networks (CNNs), the detection of defects
on rail surfaces has significantly improved. Although existing methods achieve good results …

As an essential transportation system in modern society, the significance of railway track
safety cannot be overlooked. In recent years, computer vision systems and deep learning …

In recent years, the application of computer vision for detecting rail defects has shown
promising results. However, as the accuracy of the models improves, they become more …

Salient object detection (SOD) methods typically consider SOD as a pixel-wise binary
classification problem and utilize the binary cross-entropy (BCE) loss for optimization …

Owing to extensive research on deep learning, significant progress has recently been made
in trackless surface defect detection (SDD). Nevertheless, existing algorithms face two main …

No-service rail-surface defect detection is a crucial method for assessing the quality of
railroad tracks. However, the low-contrast and dark-tone characteristics of track-surface …

The existing image segmentation tasks mainly focus on segmenting objects with specific
characteristics, such as salient, camouflaged, and meticulous objects, etc. However, the …

Rail defect detection (RDD) plays a crucial role in ensuring rail transportation safety.
Recently, bimodal algorithms have become mainstream; however, the asymmetry in the …