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[HTML][HTML] Vehicle detection and classification via YOLOv8 and deep belief network over aerial image sequences
Vehicle detection and classification are the most significant and challenging activities of an
intelligent traffic monitoring system. Traditional methods are highly computationally …
intelligent traffic monitoring system. Traditional methods are highly computationally …
YOLOrs: Object detection in multimodal remote sensing imagery
Deep-learning object detection methods that are designed for computer vision applications
tend to underperform when applied to remote sensing data. This is because contrary to …
tend to underperform when applied to remote sensing data. This is because contrary to …
Smart traffic monitoring through pyramid pooling vehicle detection and filter-based tracking on aerial images
Increased traffic density, combined with global population development, has resulted in
increasingly congested roads, increased air pollution, and increased accidents. Globally, the …
increasingly congested roads, increased air pollution, and increased accidents. Globally, the …
Target detection and classification via EfficientDet and CNN over unmanned aerial vehicles
Introduction Advanced traffic monitoring systems face significant challenges in vehicle
detection and classification. Conventional methods often require substantial computational …
detection and classification. Conventional methods often require substantial computational …
Vehicle recognition pipeline via DeepSort on aerial image datasets
Introduction Unmanned aerial vehicles (UAVs) are widely used in various computer vision
applications, especially in intelligent traffic monitoring, as they are agile and simplify …
applications, especially in intelligent traffic monitoring, as they are agile and simplify …
Vehicle detection and classification via YOLOv4 and CNN over aerial images
Advanced traffic monitoring systems face major vehicle detection and classification
challenges. Conventional methods need significant computational resources and struggle to …
challenges. Conventional methods need significant computational resources and struggle to …
DAGN: A real-time UAV remote sensing image vehicle detection framework
Z Zhang, Y Liu, T Liu, Z Lin… - IEEE Geoscience and …, 2019 - ieeexplore.ieee.org
Real-time small object detection from the remote sensing images taken by unmanned aerial
vehicles (UAVs) is a challenging but fundamental problem for many UAV applications …
vehicles (UAVs) is a challenging but fundamental problem for many UAV applications …
Multisized object detection using spaceborne optical imagery
This article addresses the highly challenging problem of vehicle detection from high-
resolution remote sensing imagery by introducing a novel medium size annotated dataset …
resolution remote sensing imagery by introducing a novel medium size annotated dataset …
An anchor-free lightweight deep convolutional network for vehicle detection in aerial images
Vehicle object detection in aerial scenes has important applications in both military and
civilian fields. Recently, deep learning has shown clear advantages in object detection, and …
civilian fields. Recently, deep learning has shown clear advantages in object detection, and …
Joint-SRVDNet: Joint super resolution and vehicle detection network
In many domestic and military applications, aerial vehicle detection and super-resolution
algorithms are frequently developed and applied independently. However, aerial vehicle …
algorithms are frequently developed and applied independently. However, aerial vehicle …