Deep learning has become a popular method for studying the semantic segmentation of
high-resolution remote sensing images (HRRSIs). Existing methods have adopted …

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 …

Owing to rapid developments in driverless technologies, vision tasks for unmanned vehicles
have gained considerable attention, particularly in multimodal-based urban scene parsing …

The existing YOLOv5‐based framework has achieved great success in the field of target
detection. However, in forest fire detection tasks, there are few high‐quality forest fire images …

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 …

Convolutional neural networks (CNNs) have shown remarkable performance in the field of
infrared small target detection. However, due to the limitation of local receptive field, existing …

Camouflaged object detection (COD) aims to identify object pixels visually embedded in the
background environment. Existing deep learning methods fail to utilize the context …

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 …

Combinatorial relational reasoning in neural networks used for object detection is usually
static; therefore, it cannot selectively fuse visual information and semantic relations, which …

Salient object detection is a challenging task in computer vision and has been used to
extract valuable information from many real scenarios. The graph-based detection approach …