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Mobile Communication Base Station Antenna Measurement Using Unmanned Aerial Vehicle

Yikui Zhai, Qirui Ke, Ying Xu, Wenbo Deng, Junying Gan, Junying Zeng, Wenlve Zhou, Fabio Scotti, Ruggero Donida Labati, Vincenzo Piuri
2019 IEEE Access  
Firstly, an antenna database, containing 19,715 images, named UAV-Antenna is constructed by image capturing with the help of UAVs flying around various base stations.  ...  Traditional base station antenna measurement methods conducted with professional worker climbing towers tend to raise safety and inefficiency concerns in practical application.  ...  FCN classifies images in pixel-to-pixel manner, thus solving the problem of image segmentation (semantic segmentation) at the semantic level.  ... 
doi:10.1109/access.2019.2935613 fatcat:3pcrdb5xerakfcywhrvotm6sxi

UAVid: A Semantic Segmentation Dataset for UAV Imagery [article]

Ye Lyu, George Vosselman, Guisong Xia, Alper Yilmaz, Michael Ying Yang
2020 arXiv   pre-print
Our UAV dataset consists of 30 video sequences capturing 4K high-resolution images in slanted views. In total, 300 images have been densely labeled with 8 classes for the semantic labeling task.  ...  In this paper, we introduce our UAVid dataset, a new high-resolution UAV semantic segmentation dataset as a complement, which brings new challenges, including large scale variation, moving object recognition  ...  Data Specification Our data acquisition and annotation methodology is designed for UAV semantic segmentation in complex urban scenes, featuring on both static and moving object recognition.  ... 
arXiv:1810.10438v2 fatcat:vqin6lw2tzeo5d4ig6ptzstuaq

A Deep CNN-Based Framework For Enhanced Aerial Imagery Registration with Applications to UAV Geolocalization

Ahmed Nassar, Karim Amer, Reda ElHakim, Mohamed ElHelw
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
A semantic shape matching algorithm is subsequently applied to extract and match meaningful shape information from both images, and use this information to improve localization accuracy.  ...  In this paper we present a novel framework for geolocalizing Unmanned Aerial Vehicles (UAVs) using only their onboard camera.  ...  [32] finds a sequence of UAV images inside a reference map to create image mosaics.  ... 
doi:10.1109/cvprw.2018.00201 dblp:conf/cvpr/NassarAEE18 fatcat:5s7rpfftaray7jvb3yyiiznziq

Remote sensing image description based on word embedding and end-to-end deep learning

Yuan Wang, Hongbing Ma, Kuerban Alifu, Yalong Lv
2021 Scientific Reports  
First, category ambiguity over large-scale regions in remote sensing images is addressed by introducing the co-occurrence matrix and global vectors for word representation to generate the word vector features  ...  remote sensing images by providing descriptions in precise and concise natural sentences.  ...  different objects and filter the non-character regions in images.  ... 
doi:10.1038/s41598-021-82704-4 pmid:33542421 fatcat:u6r4bfrcxjgppptuuvnwvill44

A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images

Qian Pan, Maofang Gao, Pingbo Wu, Jingwen Yan, Shilei Li
2021 Sensors  
The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate  ...  The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field.  ...  Image stitching was used to correct the image sequences with a certain overlap rate in space to form a seamless image with a wide field of view.  ... 
doi:10.3390/s21196540 pmid:34640873 pmcid:PMC8513082 fatcat:jmqop3j6yvc75eu7obkzoxyb4y

MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos [article]

Murari Mandal, Lav Kush Kumar, Santosh Kumar Vipparthi
2020 arXiv   pre-print
Visual data collected from Unmanned Aerial Vehicles (UAVs) has opened a new frontier of computer vision that requires automated analysis of aerial images/videos.  ...  We also analyze the motion-salient regions in the network through multiple layer visualizations. The MOR-UAVNet works online at inference as it requires only few past frames.  ...  Some of the low-level tasks in computer vision include image classification [10, 20, 21, 55] , object detection [31-33, 36, 53, 73] , semantic segmentation [7, 19] , video object segmentation [44,  ... 
arXiv:2008.01699v2 fatcat:hglqmhxmmzaqje5docjyq6q7dy

AN INTEGRATIVE OBJECT-BASED IMAGE ANALYSIS WORKFLOW FOR UAV IMAGES

Huai Yu, Tianheng Yan, Wen Yang, Hong Zheng
2016 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this work, we propose an integrative framework to process UAV images.  ...  Experimental results on processing the post-seismic UAV images of the 2013 Ya'an earthquake demonstrate the effectiveness and efficiency of our proposed method.  ...  For UAV image interpretation, this optimal segmentation scheme of UAV panorama image can be used in subsequent tasks, such as image classification, object detection and recognition, change detection.  ... 
doi:10.5194/isprs-archives-xli-b1-1085-2016 fatcat:57jolgpgljfnfej7a2tk22zl54

AN INTEGRATIVE OBJECT-BASED IMAGE ANALYSIS WORKFLOW FOR UAV IMAGES

Huai Yu, Tianheng Yan, Wen Yang, Hong Zheng
2016 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this work, we propose an integrative framework to process UAV images.  ...  Experimental results on processing the post-seismic UAV images of the 2013 Ya'an earthquake demonstrate the effectiveness and efficiency of our proposed method.  ...  For UAV image interpretation, this optimal segmentation scheme of UAV panorama image can be used in subsequent tasks, such as image classification, object detection and recognition, change detection.  ... 
doi:10.5194/isprsarchives-xli-b1-1085-2016 fatcat:yh3zi3xllnb7vk6ddnxucce7y4

The TUM-DLR Multimodal Earth Observation Evaluation Benchmark

Tobias Koch, Pablo DAngelo, Franz Kurz, Friedrich Fraundorfer, Peter Reinartz, Marco Korner
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
In order to provide data with different modalities, we observed the same scene using satellites, airplanes, unmanned aerial vehicles (UAV), and smartphones.  ...  The provided data can be freely used by anybody interested in remote sensing and earth observation and will be continuously augmented and updated.  ...  UAV Image Sequences The EOC-UAV dataset provides images of the EOC building located at the DLR campus, as indicated by the blue polygon in Fig. 1 .  ... 
doi:10.1109/cvprw.2016.92 dblp:conf/cvpr/KochdKFRK16 fatcat:3uih4sr7t5hjpkys72kk4wnga4

Enhancing Long-Range Automatic Target Recognition Using Spatial Context

Iain Rodger, Rachael Abbott, Barry Connor, Neil Robertson
2017 2017 Sensor Signal Processing for Defence Conference (SSPD)  
A highway scene is shown in image (a) and is segmented and labelled with region class information in (b).  ...  This emphasises edges and helps find region boundaries. Spatial Context: Vertical position information is taken from the segmented image regions.  ... 
doi:10.1109/sspd.2017.8233231 fatcat:v6pn5zmnnbhe7nmvjbaqrrrnzu

Robustness-Driven Exploration with Probabilistic Metric Temporal Logic [article]

Xiaotian Liu, Pengyi Shi, Sarra Alqahtani, Victor Paúl Pauca, Miles Silman
2019 arXiv   pre-print
The ability to perform autonomous exploration is essential for unmanned aerial vehicles (UAV) operating in unstructured or unknown environments where it is hard or even impossible to describe the environment  ...  However, algorithms for autonomous exploration often focus on optimizing time and coverage in a greedy fashion.  ...  has been done in 4 hours; acquiring 7200 images.  ... 
arXiv:1912.01704v1 fatcat:5ehmmirz7beyvpwh2tko3rqhmm

UAV MISSION PLANNING FOR AUTOMATIC EXPLORATION AND SEMANTIC MAPPING

Y. Dehbi, L. Klingbeil, L. Plümer
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Unmanned Aerial Vehicles (UAVs) are used for the inspection of areas which are otherwise difficult to access.  ...  The semantically rich building models are used to specify semantic user queries such as roof or facade inspection.  ...  determination of lightweight UAVs 2. local perception for the navigation of lightweight UAVs 3. 3D-navigation and obstacle detection for lightweight UAVs 4. incremental mapping from image sequences 5.  ... 
doi:10.5194/isprs-archives-xliii-b1-2020-521-2020 fatcat:tp7zjwmghngqzk5bw7v55ahvsy

REAL-TIME POWERLINE CORRIDOR INSPECTION BY EDGE COMPUTING OF UAV LIDAR DATA

S. Pu, L. Xie, M. Ji, Y. Zhao, W. Liu, L. Wang, Y. Zhao, F. Yang, D. Qiu
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Second, laser points of power line corridors are simultaneously classified into Ground, Vegetation, Tower, Cable, and Building types using semantic feature constraints during the UAV-borne LiDAR acquisition  ...  </strong> This paper presents an innovative power line corridor inspection approach using UAV LiDAR edge computing and 4G real real-time transmission.  ...  The recognition of Ground, Vegetation, Building and Cable features are similar to the semantic feature recognition method in Pu and Vosselman (2009) .  ... 
doi:10.5194/isprs-archives-xlii-2-w13-547-2019 fatcat:bl2htnk3jzf3fgrsi2v7yr3bwy

Ground Crack Recognition Based on Fully Convolutional Network with Multi-scale Input

Jian Cheng, Liang Ye, Yinan Guo, Jun Zhang, Hongbo An
2020 IEEE Access  
Though aerial images display a full view for the region containing cracks, the rugged terrain results in rich noises that have the similar characteristics with cracks in an image, such as shadow, cliff  ...  Therefore, Unmanned Aerial Vehicle(UAV) is adopted to capture aerial images of the ground crack, which provides a convenient way for goaf inspection.  ...  As we knew, the features in high-level layer represent the larger region in the original image [35] .  ... 
doi:10.1109/access.2020.2981370 fatcat:5mnex6ox75d2rh5jutl43o3ove

A Forest Fire Recognition Method Using UAV Images Based on Transfer Learning

Lin Zhang, Mingyang Wang, Yujia Fu, Yunhong Ding
2022 Forests  
However, the visual angle/distance of UAV in the process of image sampling and the limited sample size of UAV labeled images limit the accuracy of forest fire recognition based on UAV images.  ...  The model migrates the ResNet network trained on an ImageNet dataset and its initialization parameters into the target dataset of forest fire identification based on UAV images.  ...  Data Availability Statement: The FLAME Dataset in the article "Aerial Aerial Pile burn detection using Deep Learning: The FLAME Dataset" is used.  ... 
doi:10.3390/f13070975 fatcat:gip4tdijmzh2dba2z446ijry64
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