Potential UAV Landing Sites Detection through Digital Elevation Models
Analysis
- URL: http://arxiv.org/abs/2107.06921v1
- Date: Wed, 14 Jul 2021 18:13:35 GMT
- Title: Potential UAV Landing Sites Detection through Digital Elevation Models
Analysis
- Authors: Efstratios Kakaletsis, Nikos Nikolaidis
- Abstract summary: Flat areas which constitute appropriate landing zones for UAVs in normal or emergency situations result by thresholding the image gradient magnitude of the digital surface model (DSM)
Man-made structures and vegetation areas are detected and excluded from the potential landing sites.
- Score: 6.167849162878745
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, a simple technique for Unmanned Aerial Vehicles (UAVs)
potential landing site detection using terrain information through
identification of flat areas, is presented. The algorithm utilizes digital
elevation models (DEM) that represent the height distribution of an area. Flat
areas which constitute appropriate landing zones for UAVs in normal or
emergency situations result by thresholding the image gradient magnitude of the
digital surface model (DSM). The proposed technique also uses connected
components evaluation on the thresholded gradient image in order to discover
connected regions of sufficient size for landing. Moreover, man-made structures
and vegetation areas are detected and excluded from the potential landing
sites. Quantitative performance evaluation of the proposed landing site
detection algorithm in a number of areas on real world and synthetic datasets,
accompanied by a comparison with a state-of-the-art algorithm, proves its
efficiency and superiority.
Related papers
- TanDepth: Leveraging Global DEMs for Metric Monocular Depth Estimation in UAVs [5.6168844664788855]
This work presents TanDepth, a practical, online scale recovery method for obtaining metric depth results from relative estimations at inference-time.
Tailored for Unmanned Aerial Vehicle (UAV) applications, our method leverages sparse measurements from Global Digital Elevation Models (GDEM) by projecting them to the camera view.
An adaptation to the Cloth Simulation Filter is presented, which allows selecting ground points from the estimated depth map to then correlate with the projected reference points.
arXiv Detail & Related papers (2024-09-08T15:54:43Z) - TK-Planes: Tiered K-Planes with High Dimensional Feature Vectors for Dynamic UAV-based Scenes [58.180556221044235]
We present a new approach to bridge the domain gap between synthetic and real-world data for unmanned aerial vehicle (UAV)-based perception.
Our formulation is designed for dynamic scenes, consisting of small moving objects or human actions.
We evaluate its performance on challenging datasets, including Okutama Action and UG2.
arXiv Detail & Related papers (2024-05-04T21:55:33Z) - DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection.
It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor.
Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
arXiv Detail & Related papers (2023-12-11T18:38:28Z) - Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D
Object Detection [92.75961303269548]
The ground plane prior is a very informative geometry clue in monocular 3D object detection (M3OD)
We propose a Ground Plane Enhanced Network (GPENet) which resolves both issues at one go.
Our GPENet can outperform other methods and achieve state-of-the-art performance, well demonstrating the effectiveness and the superiority of the proposed approach.
arXiv Detail & Related papers (2022-11-03T02:21:35Z) - Cross-Geography Generalization of Machine Learning Methods for
Classification of Flooded Regions in Aerial Images [3.9921541182631253]
This work proposes two approaches for identifying flooded regions in UAV aerial images.
The first approach utilizes texture-based unsupervised segmentation to detect flooded areas.
The second uses an artificial neural network on the texture features to classify images as flooded and non-flooded.
arXiv Detail & Related papers (2022-10-04T13:11:44Z) - Progressive Domain Adaptation with Contrastive Learning for Object
Detection in the Satellite Imagery [0.0]
State-of-the-art object detection methods largely fail to identify small and dense objects.
We propose a small object detection pipeline that improves the feature extraction process.
We show we can alleviate the degradation of object identification in previously unseen datasets.
arXiv Detail & Related papers (2022-09-06T15:16:35Z) - A benchmark dataset for deep learning-based airplane detection: HRPlanes [3.5297361401370044]
We create a novel airplane detection dataset called High Resolution Planes (HRPlanes) by using images from Google Earth (GE)
HRPlanes include GE images of several different airports across the world to represent a variety of landscape, seasonal and satellite geometry conditions obtained from different satellites.
Our preliminary results show that the proposed dataset can be a valuable data source and benchmark data set for future applications.
arXiv Detail & Related papers (2022-04-22T23:49:44Z) - Rethinking Drone-Based Search and Rescue with Aerial Person Detection [79.76669658740902]
The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today.
We propose a novel deep learning algorithm to automate this aerial person detection (APD) task.
We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions.
arXiv Detail & Related papers (2021-11-17T21:48:31Z) - Adaptive Path Planning for UAV-based Multi-Resolution Semantic
Segmentation [26.729010176211016]
We propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations.
A key feature of our approach is a new accuracy model for deep learning-based architectures.
We evaluate our approach on the application of crop/weed segmentation in precision agriculture using real-world field data.
arXiv Detail & Related papers (2021-08-04T07:30:04Z) - Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing
Simulation-to-Real Domain Shift in LiDAR Bird's Eye View [110.83289076967895]
We present a BEV domain adaptation method based on CycleGAN that uses prior semantic classification in order to preserve the information of small objects of interest during the domain adaptation process.
The quality of the generated BEVs has been evaluated using a state-of-the-art 3D object detection framework at KITTI 3D Object Detection Benchmark.
arXiv Detail & Related papers (2021-04-22T12:47:37Z) - Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge
Detection [63.942632088208505]
We propose a post-processing algorithm to align the segmented plane masks with edges detected in the image.
This allows us to increase the accuracy of state-of-the-art approaches, while limiting ourselves to cuboid-shaped objects.
arXiv Detail & Related papers (2020-03-28T18:51:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.