In recent years, fused infrared and visible-light computer vision methods based on fully
convolutional networks (FCNs) have achieved remarkable results due to their …

Transmission line (TL) inspection plays a crucial role in maintaining a reliable electricity
supply to all regions. Computer vision methods, especially those utilizing infrared images …

Existing bi-modal salient object detection (SOD) methods primarily rely on fully supervised
training strategies that require extensive manual annotation. Undoubtedly, extensive manual …

Multimodal transmission-line detection (TLD) and other vision-related tasks in smart grids
have garnered increasing attention due to advances in deep-learning technologies and …

Multi-sensor clues have shown promise for object segmentation, but inherent noise in each
sensor, as well as the calibration error in practice, may bias the segmentation accuracy. In …

In the realm of optical remote sensing imagery, tackling the intricate task of detecting salient
objects poses challenges that expose limitations in prevailing CNN-and transformer-based …

Manual scribbles have been introduced to RGB-D Salient Object Detection (SOD) as a
credible indicator for salient regions and backgrounds, helping to strike a balance between …

Existing deep learning-based RGB-T salient object detection methods often struggle with
effectively fusing RGB and thermal features. Therefore, obtaining high-quality features and …

How to explore the interaction between RGB and thermal infrared modalities is critical to the
success of RGB-T salient object detection (SOD). Most existing methods collect and convey …

Transmission line detection (TLD) plays a crucial role in ensuring the safety and stability of
electricity supply. Applying RGB-thermal convolutional neural networks (CNNs) to …