[HTML][HTML] A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas

J Terven, DM Córdova-Esparza… - Machine Learning and …, 2023 - mdpi.com
YOLO has become a central real-time object detection system for robotics, driverless cars,
and video monitoring applications. We present a comprehensive analysis of YOLO's …

Foundation models for generalist medical artificial intelligence

M Moor, O Banerjee, ZSH Abad, HM Krumholz… - Nature, 2023 - nature.com
The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI)
models is likely to usher in newfound capabilities in medicine. We propose a new paradigm …

Large selective kernel network for remote sensing object detection

Y Li, Q Hou, Z Zheng, MM Cheng… - Proceedings of the …, 2023 - openaccess.thecvf.com
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 …

Rtmdet: An empirical study of designing real-time object detectors

C Lyu, W Zhang, H Huang, Y Zhou, Y Wang… - arXiv preprint arXiv …, 2022 - arxiv.org
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 …

Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles

H Li, J Li, H Wei, Z Liu, Z Zhan, Q Ren - arXiv preprint arXiv:2206.02424, 2022 - arxiv.org
Object detection is a significant downstream task in computer vision. For the on-board edge
computing platforms, a giant model is difficult to achieve the real-time detection requirement …

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 …

Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities

L Zhang, L Zhang - IEEE Geoscience and Remote Sensing …, 2022 - ieeexplore.ieee.org
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

Oriented R-CNN for object detection

X Xie, G Cheng, J Wang, X Yao… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Current state-of-the-art two-stage detectors generate oriented proposals through time-
consuming schemes. This diminishes the detectors' speed, thereby becoming the …

Mmrotate: A rotated object detection benchmark using pytorch

Y Zhou, X Yang, G Zhang, J Wang, Y Liu… - Proceedings of the 30th …, 2022 - dl.acm.org
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 …

A comprehensive review on deep learning based remote sensing image super-resolution methods

P Wang, B Bayram, E Sertel - Earth-Science Reviews, 2022 - Elsevier
Satellite imageries are an important geoinformation source for different applications in the
Earth Science field. However, due to the limitation of the optic and sensor technologies and …