FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery
With the rapid development of deep learning, many deep learning-based approaches have
made great achievements in object detection tasks. It is generally known that deep learning …
made great achievements in object detection tasks. It is generally known that deep learning …
Object detection in 20 years: A survey
Object detection, as of one the most fundamental and challenging problems in computer
vision, has received great attention in recent years. Over the past two decades, we have …
vision, has received great attention in recent years. Over the past two decades, we have …
Large selective kernel network for remote sensing object detection
Recent research on remote sensing object detection has largely focused on improving the
representation of oriented bounding boxes but has overlooked the unique prior knowledge …
representation of oriented bounding boxes but has overlooked the unique prior knowledge …
Rtmdet: An empirical study of designing real-time object detectors
In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO
series and is easily extensible for many object recognition tasks such as instance …
series and is easily extensible for many object recognition tasks such as instance …
Towards large-scale small object detection: Survey and benchmarks
With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …
prominent advances in past years. However, such prosperity could not camouflage the …
Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs
U Nepal, H Eslamiat - Sensors, 2022 - mdpi.com
In-flight system failure is one of the major safety concerns in the operation of unmanned
aerial vehicles (UAVs) in urban environments. To address this concern, a safety framework …
aerial vehicles (UAVs) in urban environments. To address this concern, a safety framework …
Oriented R-CNN for object detection
Current state-of-the-art two-stage detectors generate oriented proposals through time-
consuming schemes. This diminishes the detectors' speed, thereby becoming the …
consuming schemes. This diminishes the detectors' speed, thereby becoming the …
Mmrotate: A rotated object detection benchmark using pytorch
We present an open-source toolbox, named MMRotate, which provides a coherent algorithm
framework of training, inferring, and evaluation for the popular rotated object detection …
framework of training, inferring, and evaluation for the popular rotated object detection …
Advancing plain vision transformer toward remote sensing foundation model
Large-scale vision foundation models have made significant progress in visual tasks on
natural images, with vision transformers (ViTs) being the primary choice due to their good …
natural images, with vision transformers (ViTs) being the primary choice due to their good …
Redet: A rotation-equivariant detector for aerial object detection
Recently, object detection in aerial images has gained much attention in computer vision.
Different from objects in natural images, aerial objects are often distributed with arbitrary …
Different from objects in natural images, aerial objects are often distributed with arbitrary …