Humans are able to learn to recognize new objects even from a few examples. In contrast,
training deep-learning-based object detectors requires huge amounts of annotated data. To …

Object detection as a subfield within computer vision has achieved remarkable progress,
which aims to accurately identify and locate a specific object from images or videos. Such …

Object detection is essential to the interpretation of optical remote sensing images and can
serve as a foundation for research into additional visual tasks that utilize remote sensing …

Plant detection and counting play a crucial role in modern agriculture, providing vital
references for precision management and resource allocation. This study follows the …

Deep learning (DL)-based object detection algorithms have gained impressive
achievements in natural images and have gradually matured in recent years. However …

Object detection (OD) is an essential and fundamental task in computer vision (CV) and
satellite image processing. Existing deep learning methods have achieved impressive …

Remote sensing object detection (RSOD), one of the most fundamental and challenging
tasks in the remote sensing field, has received long-standing attention. In recent years, deep …

Remote sensing image object detection holds significant research value in resources and
the environment. Nevertheless, complex background information and considerable size …

Introduction: Object detection in remotely sensed satellite images is critical to socio-
economic, bio-physical, and environmental monitoring, necessary for the prevention of …

Most object detection methods based on remote sensing images are generally dependent
on a large amount of high-quality labeled training data. However, due to the slow acquisition …