Human detection is aimed at automatically labeling specific semantic objects in high-
resolution images, which is a key problem in the post-disaster search and rescue (SAR) …

In recent years, the realm of deep learning has witnessed significant advancements,
particularly in object detection algorithms. However, the unique challenges posed by remote …

Due to the limited semantic information extraction with small objects and difficulty in
distinguishing similar targets, it brings great challenges to target detection in remote sensing …

In recent years, the denoising diffusion model has achieved remarkable success in image
segmentation modeling. With its powerful nonlinear modeling capabilities and superior …

Few-class object detection is a critical task in numerous scenes, such as autonomous
driving and intelligent surveillance. The current researches mainly focus on the correlation …

Amid the swift evolution of advanced technologies such as autonomous robotics, edge
computing, and the Internet of Things, Intelligent Agriculture Management Systems (IAMS) …

Achieving accurate object tracking against noise spoofing interference caused by similar
backgrounds, similar objects, occlusion, or illumination variations is a challenge, especially …

Existing one-stage object detectors are commonly implemented in a multi-task learning
based manner, which simultaneously solves two different sub-tasks: object classification and …

Successfully detecting small pedestrians through vehicle camera sensors would greatly
facilitate the development of autonomous driving safety applications. However, the existing …

This study focuses on enhancing the accuracy and efficiency of semantic analysis systems
for recognizing moving objects within video sequences. The primary aim is to improve object …