Detecting and recognizing objects are crucial steps in interpreting remote sensing images.
At present, deep learning methods are predominantly employed for detecting objects in …

In view of the existence of remote sensing images with large variations in spatial resolution,
small and dense objects, and the inability to determine the direction of motion, all these …

Object detection has drawn the attention of many researchers due to its wide application in
computer vision-related applications. In this paper, a novel model is proposed for object …

There have been significant advancements in deep learning based object detection
algorithms, which have found widespread applications across various fields, including …

Deep learning-based object detection in remote sensing images is an important yet
challenging task due to a series of difficulties, such as complex geometry scene, dense …

It is a challenging problem to detect small and dense objects in remote sensing images. To
address these problems, this paper presents a novel network based on YOLOv3 to detect …

In recent years, deep learning techniques have been increasingly applied to the detection of
remote sensing images. However, the substantial size variation and dense distribution of …

Object detection is the key task for many Unmanned Aerial Vehicles (UAVs) tasks.
Compared with images captured from ground-based angles, images captured by UAVs are …

An efficient, low-complexity, and anchor-free object detector based on the state-of-the-art
YOLO framework is proposed in this paper, which can be implemented in real time on edge …

Aiming at the challenges of low detection accuracy, susceptibility to complex background
interference, difficulty in detecting small objects, and multi-scale object issues in aerial …