Abstract: Few-shot object detection (FSOD) in remote sensing imagery aims to achieve accurate detection of novel object categories with limited training samples. However, current mainstream transfer ...

Abstract: In recent years, optical remote sensing image salient object detection (ORSI-SOD) has made substantial progress. Nevertheless, it remains an open-ended research area with complex challenges.

Abstract: Adverse weather conditions significantly impact the performance of autonomous driving object detection systems, leading to reduced detection accuracy and an increased false detection rate.

Abstract: Fine-grained object detection (FOD) is essential in many remote sensing image interpretation tasks. Existing FOD methods have achieved remarkable progress in modeling discriminative features ...

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: Small uncrewed autonmous vehicles (UAVs) equipped with deep learning models are increasingly used to detect small objects both on the ground and in aerial environments. Since small objects ...

Abstract: The perception of night scenes is of crucial importance for driving safety. In the dimly lit night environment, as the visibility of objects decreases, both experienced and inexperienced ...

Abstract: Detecting small objects and managing occlusions remain persistent challenges in object detection tasks, particularly in complex scenarios with diverse environments or densely packed scenes.

Abstract: Millimeter-wave radar object detection has become pivotal for autonomous driving systems requiring all-weather reliability. While conventional CFAR methods face limitations in classification ...

Abstract: Traditional 3D object detectors, whether fully-, semi-, or weakly-supervised, rely heavily on extensive human annotations. In contrast, this paper introduces an unsupervised 3D object ...