287 Hits in 3.5 sec

Towards Detection of Sheep Onboard a UAV [article]

Farah Sarwar, Anthony Griffin, Saeed Ur Rehman, Timotius Pasang
2020 arXiv   pre-print
In this work we consider the task of detecting sheep onboard an unmanned aerial vehicle (UAV) flying at an altitude of 80 m.  ...  Although deep learning strategies have gained enormous popularity in the last decade and are now extensively used for object detection in many fields, state-of-the-art detectors perform poorly in the case  ...  In this work, we tackle the problem of detecting and counting sheep from unmanned aerial vehicle (UAV) imagery.  ... 
arXiv:2004.02758v1 fatcat:hwmxkgxwnbd3xe4ixuaxzpj7vy

DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery

Maryam Rahnemoonfar, Dugan Dobbs, Masoud Yari, and Michael J. Starek
2019 Remote Sensing  
Recent deep-learning counting techniques revolve around two distinct features of data—sparse data, which favors detection networks, or dense data where density map networks are used.  ...  Raw aerial images represent sparse distributions of data in most situations.  ...  any manual cropping of imagery and counting is performed automatically in an end-to-end learning procedure on optical imagery.  ... 
doi:10.3390/rs11091128 fatcat:bmersfs2grcdbnjaecwyyrjh4y


C. Tsouvaltsidis, N. Zaid Al Salem, G. Benari, D. Vrekalic, B. Quine
2015 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
aerial vehicle (UAV) system.  ...  However, limitations in the volume of data due to onboard power constraints and a lack of an onboard camera system make it very difficult to verify these objectives using ground truth.  ...  Vrekalic's guidance was instrumental in aiding to design a UAV system that would be rugged enough to perform field work and gentle enough for a beginner to fly.  ... 
doi:10.5194/isprsarchives-xl-1-w4-25-2015 fatcat:bbgu3eu7tvca7jyz7rqqbgl4qy

Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment [article]

Jithin Jagannath, Anu Jagannath, Sean Furman, Tyler Gwin
2020 arXiv   pre-print
Therefore, in this chapter, we discuss how some of the advances in machine learning, specifically deep learning and reinforcement learning can be leveraged to develop next-generation autonomous UAS.  ...  We then provide an overview of some of the key deep learning and reinforcement learning techniques discussed throughout this chapter.  ...  The UAV is a Parrot AR-Drone 2.0 with a 720p forward-facing camera onboard.  ... 
arXiv:2009.03349v2 fatcat:5ylreoukrfcrtorzzp44mntjum

When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges

Xuan Zhou, Ruimin Ke, Hao Yang, Chenxi Liu
2021 Applied Sciences  
The key challenges in ITS sensing and future directions with the integration of edge computing are discussed.  ...  In this paper, we focus on a critical part of ITS, i.e., sensing, and conducting a review on the recent advances in ITS sensing and EC applications in this field.  ...  [213, 214] designed two of the popular methods for road detection in UAV imagery.  ... 
doi:10.3390/app11209680 fatcat:li4mubzsbncjbcqfewgr5wemeq

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)  
The framework exploits the abundance of satellite imagery, along with established computer vision and deep learning methods, to locate the UAV in a satellite imagery map.  ...  In this paper we present a novel framework for geolocalizing Unmanned Aerial Vehicles (UAVs) using only their onboard camera.  ...  In autonomous vision-only UAV navigation, UAV camera feed is compared with aerial/satellite imagery to consequently infer drone location.  ... 
doi:10.1109/cvprw.2018.00201 dblp:conf/cvpr/NassarAEE18 fatcat:5s7rpfftaray7jvb3yyiiznziq

Autonomous, Onboard Vision-Based Trash and Litter Detection in Low Altitude Aerial Images Collected by an Unmanned Aerial Vehicle

Marek Kraft, Mateusz Piechocki, Bartosz Ptak, Krzysztof Walas
2021 Remote Sensing  
This paper proposes a low-cost solution enabling the localisation of trash and litter objects in low altitude imagery collected by an unmanned aerial vehicle (UAV) during an autonomous patrol mission.  ...  The dataset was used to test a range of embedded devices enabling the deployment of deep neural networks for inference onboard the UAV.  ...  Acknowledgments: The authors would like to thank Adam Bondyra and Dominik Pieczyński for their help with data collection. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13050965 doaj:84d38d75156643f0a0292ced69366edc fatcat:kzcr77k3ybbk3plsvpgvx6gkhe

Mapping and 3D modelling using quadrotor drone and GIS software

Widodo Budiharto, Edy Irwansyah, Jarot S. Suroso, Andry Chowanda, Heri Ngarianto, Alexander Agung Santoso Gunawan
2021 Journal of Big Data  
These constraints are currently obtaining solutions in line with the development of improved UAV drone technology with a wider range and imaging sensors that can be used.  ...  The drone used has advantages in a wider range of areas with adequate power support.  ...  Mapping and 3D modelling A UAV or un-crewed aerial vehicle, commonly known as a drone is an aircraft without a human pilot onboard and a type of unmanned vehicle [11] .  ... 
doi:10.1186/s40537-021-00436-8 fatcat:2p4f46h2jjhqtgkq65r7ueedk4

Unmanned Aerial Vehicles for Crowd Monitoring and Analysis

Muhammad Afif Husman, Waleed Albattah, Zulkifli Zainal Abidin, Yasir Mohd. Mustafah, Kushsairy Kadir, Shabana Habib, Muhammad Islam, Sheroz Khan
2021 Electronics  
Vehicle specifications, onboard sensors, power management, and an analysis algorithm are critically reviewed and discussed.  ...  From preventing stampede in high concentration crowds to estimating crowd density and to surveilling crowd movements, crowd monitoring and analysis have long been employed in the past by authorities and  ...  This has achieved using human pose estimation carried out via the proposed ScatterNet Hybrid Deep Learning (SHDL) network algorithm.  ... 
doi:10.3390/electronics10232974 fatcat:2qlnspwehrdfra4s37c5odrcy4

Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery

Huasheng Huang, Jizhong Deng, Yubin Lan, Aqing Yang, Lei Zhang, Sheng Wen, Huihui Zhang, Yali Zhang, Yusen Deng
2019 Applied Sciences  
In this work, the UAV imagery acquisition and ground investigation were conducted in Central China on April 22th, 2017.  ...  The objective of this paper is to evaluate the potential of unmanned aerial vehicle (UAV) remote sensing for HLB detection.  ...  Compared with satellite and aircraft remote sensing, unmanned aerial vehicle (UAV) can fly at a low altitude and capture high resolution imagery [12] , which would provide more detailed spatial information  ... 
doi:10.3390/app9030558 fatcat:xji3snn5ijbdhgb4637n57ceaq

Deep TEC: Deep Transfer Learning with Ensemble Classifier for Road Extraction from UAV Imagery

J. Senthilnath, Neelanshi Varia, Akanksha Dokania, Gaotham Anand, Jón Atli Benediktsson
2020 Remote Sensing  
The proposed deep TEC performs road extraction on UAV imagery in two stages, namely, deep transfer learning and ensemble classifier.  ...  The paper focuses on a novel method which consists of deep TEC (deep transfer learning with ensemble classifier) for road extraction using UAV imagery.  ...  In this paper, a new deep transfer learning with ensemble classifier (deep TEC) is proposed for road extraction using UAV imagery. The proposed method deep TEC consists of two stages.  ... 
doi:10.3390/rs12020245 fatcat:dsdonwoaajfypldvleelwgjijy

Unmanned Aerial Vehicles for Wildland Fires: Sensing, Perception, Cooperation and Assistance

Moulay A. Akhloufi, Andy Couturier, Nicolás A. Castro
2021 Drones  
In this paper, previous works related to the use of UAV in wildland fires are reviewed. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered.  ...  To tackle this limitation, unmanned aerial vehicles (UAV) and unmanned aerial systems (UAS) were proposed.  ...  The majority of them have not been integrated with UAVs. In recent years, deep learning algorithms have shown impressive results in different areas.  ... 
doi:10.3390/drones5010015 fatcat:lxalqmrxyzfoneck7aobz2b62m

Development of a Miniaturized Mobile Mapping System for In-Row, Under-Canopy Phenotyping

Raja Manish, Yi-Chun Lin, Radhika Ravi, Seyyed Meghdad Hasheminasab, Tian Zhou, Ayman Habib
2021 Remote Sensing  
Plant centers and plant count with an accuracy in the 90% range have been achieved.  ...  The system is based on an unmanned ground vehicle (UGV) for in-row, under-canopy data acquisition to deliver accurately georeferenced 2D and 3D products.  ...  [20] conducted a study using UAV-based imagery for automated panicle counting in sorghum fields based on a deep learning framework for semantic segmentation.  ... 
doi:10.3390/rs13020276 fatcat:i53e5dogybhiphthwbvipfmsfu

A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

Adrian Carrio, Carlos Sampedro, Alejandro Rodriguez-Ramos, Pascual Campoy
2017 Journal of Sensors  
We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions.  ...  In addition, a detailed explanation of the main deep learning techniques is provided.  ...  from UAVs [71] , assisting avalanche search and rescue operations with UAV imagery [72] , and terrorist identification [73] .  ... 
doi:10.1155/2017/3296874 fatcat:xyijdhd47vanbk6zkeabzr7iuu

Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey

Jia Liu, Jianjian Xiang, Yongjun Jin, Renhua Liu, Jining Yan, Lizhe Wang
2021 Remote Sensing  
These devices are with built-in processors enabling onboard analytics or AI (e.g., UAVs and Internet of Things gateways).  ...  Furthermore, deep learning (DL) has been successfully applied in agricultural applications such as weed detection, crop pest and disease detection, etc. as an intelligent tool.  ...  Deep Learning in Precision Agriculture with UAV Remote Sensing Deep Learning Methods in Precision Agriculture DL is a subset of artificial neural network (ANN) methods in machine learning.  ... 
doi:10.3390/rs13214387 fatcat:amrm5blon5hmhnk7arme2vsqwq
« Previous Showing results 1 — 15 out of 287 results