Leveraging the extensive training data from SA-1B, the segment anything model (SAM)
demonstrates remarkable generalization and zero-shot capabilities. However, as a category …

Issues, such as insufficient feature representation and background confusion, make
detection tasks for small object in remote sensing arduous. Particularly, when the algorithm …

Existing model evaluation tools mainly focus on evaluating classification models, leaving a
gap in evaluating more complex models, such as object detection. In this paper, we develop …

Due to the complex imaging mechanism of SAR images and the lack of multi-angle and
multi-parameter real scene SAR target data, the generalization performance of existing deep …

This study presents a comprehensive analysis and improvement of the YOLOv8-n algorithm
for object detection, focusing on the integration of Wasserstein Distance Loss, FasterNext …

The threat of forest fires to human life and property causes significant damage to human
society. Early signs, such as small fires and smoke, are often difficult to detect. As a …

Utilizing Unmanned Aerial Vehicles (UAV) and instance segmentation for construction site
monitoring (such as construction machinery and operation surfaces) offers a significant leap …

The Segment Anything Model (SAM), a foundational model designed for promptable
segmentation tasks, demonstrates exceptional generalization capabilities, making it highly …

For many safety-related applications such as hazard mapping or road management, well-
documented avalanche events are crucial. Nowadays, despite the variety of research …

Weed detection technology is of paramount significance in achieving automation and
intelligence in weed control. Nevertheless, it grapples with several formidable challenges …