Researchers have examined the challenge of detecting and classifying dynamic road obstacles for autonomous driving systems ...

Abstract: Monocular 3D object detection has gained considerable attention because of its cost-effectiveness and practical applicability, particularly in autonomous driving and robotics. Most of ...

Abstract: Small object detection in remote sensing images remains challenging due to limited feature resolution and complex backgrounds. Conventional detectors, due to fixed receptive fields and ...

Abstract: Object Object detection, a fundamental task in computer vision, has undergone a revolutionary transformation with the advent of deep learning. This paper provides a comprehensive review of ...

Abstract: The performance of existing object detection algorithms significantly degrades when applied to low-resolution infrared (IR) images captured by unmanned aerial vehicles (UAVs), which suffers ...

Abstract: Tiny-object detection is increasingly crucial in fields such as remote sensing, traffic monitoring, and robotics. Inspired by human visual perception, the attention mechanism has become a ...

Abstract: Ensuring reliable object detection in adverse conditions is paramount for safe autonomous driving. While cameras and LiDAR struggle in such scenarios, Frequency Modulated Continuous Wave ...

Abstract: Maintaining security is of prime importance in public spaces such as markets, train stations, and airports. Such situations demand reliable and advanced automated surveillance systems. This ...

Abstract: With the emergence of various large-scale deep-learning models, in remote sensing images, the object detection effect is also plagued by complex calculations, high costs, and high ...

Abstract: Space noncooperative object detection (SNCOD) is an essential part of space situation awareness. The localization and segmentation capabilities of the salient object detection (SOD) method ...