检索结果-内蒙古大学图书馆

2025 IEEE/CVF Winter Conference on Applications of computer vision, WACV 2025

作者： Lenhard, Tamara R. Weinmann, Andreas Franke, Kai Koch, Tobias Germany University of Applied Sciences Darmstadt Working Group Algorithms for Computer Vision Imaging and Data Analysis Germany

ISBN: (纸本)9798331510831

Developing robust drone detection systems is often constrained by the limited availability of large-scale annotated training data and the high costs associated with real-world data collection. However, leveraging synthetic data generated via game engine-based simulations provides a promising and cost-effective solution to overcome this issue. Therefore, we present SynDronevision, a synthetic dataset specifically designed for RGB-based drone detection in surveillance applications. Featuring diverse backgrounds, lighting conditions, and drone models, SynDronevision offers a comprehensive training foundation for deep learning algorithms. To evaluate the dataset's effectiveness, we perform a comparative analysis across a selection of recent YOLO detection models. Our findings demonstrate that SynDronevision is a valuable resource for real-world data enrichment, achieving notable enhancements in model performance and robustness, while signifcantly reducing the time and costs of real-world data acquisition. SynDronevision can be accessed at https://***/records/13360116. © 2025 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

SynDronevision: A Synthetic Dataset for Image-Based Drone Detection

SynDroneVision: A Synthetic Dataset for Image-Based Drone De...

引用

IEEE Workshop on Applications of computer vision (WACV)

作者： Tamara R. Lenhard Andreas Weinmann Kai Franke Tobias Koch Institute for the Protection of Terrestrial Infrastructures German Aerospace Center (DLR) Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis University of Applied Sciences Darmstadt Germany

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

关键词： YOLO Training Costs Surveillance Training data Lighting Data models Robustness Drones Synthetic data

来源：评论

学校读者我要写书评

暂无评论

SynDronevision: A Synthetic Dataset for Image-Based Drone Detection

arXiv

引用

arXiv 2024年

作者： Lenhard, Tamara R. Weinmann, Andreas Franke, Kai Koch, Tobias Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis University of Applied Sciences Darmstadt Germany

Developing robust drone detection systems is often constrained by the limited availability of large-scale annotated training data and the high costs associated with real-world data collection. However, leveraging synthetic data generated via game engine-based simulations provides a promising and cost-effective solution to overcome this issue. Therefore, we present SynDronevision, a synthetic dataset specifically designed for RGB-based drone detection in surveillance applications. Featuring diverse backgrounds, lighting conditions, and drone models, SynDronevision offers a comprehensive training foundation for deep learning algorithms. To evaluate the dataset's effectiveness, we perform a comparative analysis across a selection of recent YOLO detection models. Our findings demonstrate that SynDronevision is a valuable resource for real-world data enrichment, achieving notable enhancements in model performance and robustness, while significantly reducing the time and costs of real-world data acquisition. SynDronevision will be publicly released upon paper acceptance. © 2024, CC BY.

关键词： Aircraft detection

来源：评论

学校读者我要写书评

暂无评论

YOLO-FEDER FusionNet: A Novel Deep Learning Architecture for Drone Detection

arXiv

引用

arXiv 2024年

作者： Lenhard, Tamara R. Weinmann, Andreas Jäger, Stefan Koch, Tobias Sankt Augustin Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis University of Applied Sciences Darmstadt Darmstadt Germany

Predominant methods for image-based drone detection frequently rely on employing generic object detection algorithms like YOLOv5. While proficient in identifying drones against homogeneous backgrounds, these algorithms often struggle in complex, highly textured environments. In such scenarios, drones seamlessly integrate into the background, creating camouflage effects that adversely affect the detection quality. To address this issue, we introduce a novel deep learning architecture called YOLO-FEDER FusionNet. Unlike conventional approaches, YOLO-FEDER FusionNet combines generic object detection methods with the specialized strength of camouflage object detection techniques to enhance drone detection capabilities. Comprehensive evaluations of YOLO-FEDER FusionNet show the efficiency of the proposed model and demonstrate substantial improvements in both reducing missed detections and false alarms. © 2024, CC BY-NC-SA.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

YOLO-Feder Fusionnet: A Novel Deep Learning Architecture for Drone Detection

YOLO-Feder Fusionnet: A Novel Deep Learning Architecture for...

引用

IEEE International Conference on Image Processing

作者： Tamara R. Lenhard Andreas Weinmann Stefan Jäger Tobias Koch German Aerospace Center (DLR) Institute for the Protection of Terrestrial Infrastructures Sankt Augustin Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis University of Applied Sciences Darmstadt Darmstadt Germany

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

关键词： YOLO Image processing Deep architecture Detectors Streaming media Neck Complexity theory

来源：评论

学校读者我要写书评

暂无评论

Blessemflood21: Advancing Flood Analysis with a High-Resolution Georeferenced Dataset for Humanitarian Aid Support

Blessemflood21: Advancing Flood Analysis with a High-Resolut...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Vladyslav Polushko Alexander Jenal Jens Bongartz Immanuel Weber Damjan Hatic Ronald Rösch Thomas März Markus Rauhut Andreas Weinmann Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained through drones, for rapid situational analysis to plan life-saving actions. computer vision tools are needed to support task force experts on-site in the evaluation of the imagery to improve their efficiency and to allocate resources strategically. We introduce the BlessemFlood21 dataset to stimulate research on efficient flood detection tools. The imagery was acquired during the 2021 Erftstadt-Blessem flooding event and consists of high-resolution and georeferenced RGB-NIR images. In the resulting RGB dataset, the images are supplemented with detailed water masks, obtained via a semi-supervised human-in-the-loop technique, where in particular the NIR information is leveraged to classify pixels as either water or non-water. We evaluate our dataset by training and testing established Deep Learning models for semantic segmentation. With BlessemFlood21 we provide labeled high-resolution RGB data and a baseline for further development of algorithmic solutions tailored to flood detection in RGB imagery.

关键词： Deep learning Training Semantic segmentation Human in the loop Sensors Floods Task analysis

来源：评论

学校读者我要写书评

暂无评论

BLESSEMFLOOD21: ADVANCING FLOOD ANALYSIS WITH A HIGH-RESOLUTION GEOREFERENCED DATASET FOR HUMANITARIAN AID SUPPORT

arXiv

引用

arXiv 2024年

作者： Polushko, Vladyslav Jenal, Alexander Bongartz, Jens Weber, Immanuel Hatic, Damjan Rösch, Ronald März, Thomas Rauhut, Markus Weinmann, Andreas Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Hochschule Darmstadt Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

关键词： Floods

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：