An Acquisition and Distribution System for Situational Awareness

2003 ◽  
Author(s):  
William E. Green ◽  
Paul Y. Oh ◽  
Seunghyun Yoon
Keyword(s):  
Edge Detection ◽  
3D Reconstruction ◽  
Distribution System ◽  
Situational Awareness ◽  
Aerial Images ◽  
Mechanism Synthesis ◽  
Aerial Robots ◽  
First Response ◽  
Time Required ◽  
Aerial Platform

In times of disaster acquiring aerial images is challenging. Runways may be crippled thus denying conventional aircraft in the area from taking off. Also the time required to schedule a satellite fly-by may delay first response efforts. Man backpackable aerial robots can be carried close to the disaster site and flown to capture aerial images. This paper integrates mechatronics, intelligent sensing, and mechanism synthesis in a teleoperable kite-mounted camera. Rapidly deployable, transportable by foot, easy to fly and affordable, our system can quickly acquire, process and distribute aerial images. Images mosaicing edge detection, 3D reconstruction and geo-referencing resulting from images acquired by our aerial platform are also presented.

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

Improving Power Distribution System Situational Awareness Using Visual Analytics

2018 ◽  
Author(s):  
Tumininu Lawanson ◽  
Roozbeh Karandeh ◽  
Valentina Cecchi ◽  
Zachary Wartell ◽  
Isaac Cho
Keyword(s):  
Power Distribution ◽  
Distribution System ◽  
Visual Analytics ◽  
Situational Awareness ◽  
Power Distribution System

scholarly journals Computerized or Automated Object Recognition and Analysis of Pattern Matching in Runways Using Surface Track Data

2021 ◽  
Vol 23 (11) ◽  
pp. 159-165
Author(s):  
JAYANTH DWIJESH H P ◽  
◽  
SANDEEP S V ◽  
RASHMI S ◽  
◽  
...  
Keyword(s):  
Edge Detection ◽  
Object Detection ◽  
Pattern Matching ◽  
Aerial Images ◽  
3 Dimensional ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Detection Analysis ◽  
Efficient Detection ◽  
Minimal Damage

In today’s world, accurate and fast information is vital for safe aircraft landings. The purpose of an EMAS (Engineered Materials Arresting System) is to prevent an aeroplane from overrunning with no human injury and minimal damage to the aircraft. Although various algorithms for object detection analysis have been developed, only a few researchers have examined image analysis as a landing assist. Image intensity edges are employed in one system to detect the sides of a runway in an image sequence, allowing the runway’s 3-dimensional position and orientation to be approximated. A fuzzy network system is used to improve object detection and extraction from aerial images. In another system, multi-scale, multiplatform imagery is used to combine physiologically and geometrically inspired algorithms for recognizing objects from hyper spectral and/or multispectral (HS/MS) imagery. However, the similarity in the top view of runways, buildings, highways, and other objects is a disadvantage of these methods. We propose a new method for detecting and tracking the runway based on pattern matching and texture analysis of digital images captured by aircraft cameras. Edge detection techniques are used to recognize runways from aerial images. The edge detection algorithms employed in this paper are the Hough Transform, Canny Filter, and Sobel Filter algorithms, which result in efficient detection.

scholarly journals Analysis of the Sensitivity of Extended Kalman Filter-Based Inertia Estimation Method to the Assumed Time of Disturbance

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 483 ◽  
Author(s):  
Davide del Giudice ◽  
Samuele Grillo
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Situational Awareness ◽  
Renewable Energy Sources ◽  
Estimation Method ◽  
Electric Power System ◽  
Estimation Methods ◽  
Frequency Regulation ◽  
Time Required ◽  
Primary Frequency

The frequency behavior of an electric power system right after a power imbalance is determined by its inertia constant. The current shift in generation mix towards renewable energy sources is leading to a smaller and more variable inertia, thereby compromising the frequency stability of modern grids. Therefore, real-time inertia estimation methods would be beneficial for grid operators, as their situational awareness would be enhanced. This paper focuses on an inertia estimation method specifically tailored for synchronous generators, based on the extended Kalman filter (EKF). Such a method should be started at the time of disturbance, which must be estimated accurately, otherwise additional errors could be introduced in the inertia estimation process. In this paper, the sensitivity of the EKF-based inertia estimation method to the assumed time of disturbance is analyzed. It is shown that such sensitivity is influenced by the initially assumed inertia constant, the use time of the filter and by the time required for primary frequency regulation to be activated.

scholarly journals Deep Learning Segmentation and 3D Reconstruction of Road Markings Using Multiview Aerial Imagery

2019 ◽  
Vol 8 (1) ◽  
pp. 47 ◽  
Author(s):  
Franz Kurz ◽  
Seyed Azimi ◽  
Chun-Yu Sheu ◽  
Pablo d’Angelo
Keyword(s):  
3D Reconstruction ◽  
Autonomous Vehicles ◽  
Automatic Segmentation ◽  
Image Data ◽  
Autonomous Driving ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Aerial Image ◽  
Matching Problem ◽  
Convolutional Network

The 3D information of road infrastructures is growing in importance with the development of autonomous driving. In this context, the exact 2D position of road markings as well as height information play an important role in, e.g., lane-accurate self-localization of autonomous vehicles. In this paper, the overall task is divided into an automatic segmentation followed by a refined 3D reconstruction. For the segmentation task, we applied a wavelet-enhanced fully convolutional network on multiview high-resolution aerial imagery. Based on the resulting 2D segments in the original images, we propose a successive workflow for the 3D reconstruction of road markings based on a least-squares line-fitting in multiview imagery. The 3D reconstruction exploits the line character of road markings with the aim to optimize the best 3D line location by minimizing the distance from its back projection to the detected 2D line in all the covering images. Results showed an improved IoU of the automatic road marking segmentation by exploiting the multiview character of the aerial images and a more accurate 3D reconstruction of the road surface compared to the semiglobal matching (SGM) algorithm. Further, the approach avoids the matching problem in non-textured image parts and is not limited to lines of finite length. In this paper, the approach is presented and validated on several aerial image data sets covering different scenarios like motorways and urban regions.

scholarly journals Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Julián Tachella ◽  
Yoann Altmann ◽  
Nicolas Mellado ◽  
Aongus McCarthy ◽  
Rachael Tobin ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
Single Photon ◽  
Three Dimensional ◽  
Lidar Data ◽  
Computational Framework ◽  
Processing Times ◽  
Outdoor Scenes ◽  
3D Scene ◽  
Time Required

Abstract Single-photon lidar has emerged as a prime candidate technology for depth imaging through challenging environments. Until now, a major limitation has been the significant amount of time required for the analysis of the recorded data. Here we show a new computational framework for real-time three-dimensional (3D) scene reconstruction from single-photon data. By combining statistical models with highly scalable computational tools from the computer graphics community, we demonstrate 3D reconstruction of complex outdoor scenes with processing times of the order of 20 ms, where the lidar data was acquired in broad daylight from distances up to 320 metres. The proposed method can handle an unknown number of surfaces in each pixel, allowing for target detection and imaging through cluttered scenes. This enables robust, real-time target reconstruction of complex moving scenes, paving the way for single-photon lidar at video rates for practical 3D imaging applications.

Model-Based 3D Reconstruction of Buildings from Multiple Aerial Images

Annals of GIS ◽  
2002 ◽  
Vol 8 (1) ◽  
pp. 16-23
Author(s):  
Yi-Hsing Tseng ◽  
Sendo Wang
Keyword(s):  
3D Reconstruction ◽  
Aerial Images ◽  
Model Based

Detection and 3D Reconstruction of Buildings from Aerial Images

2019 ◽  
Vol 45 (6) ◽  
pp. 311-318
Author(s):  
L. V. Novotortsev ◽  
A. G. Voloboy
Keyword(s):  
3D Reconstruction ◽  
Aerial Images

scholarly journals Follow-Up Control and Image Recognition of Neck Level for Standard Metal Gauge

2020 ◽  
Vol 10 (18) ◽  
pp. 6624
Author(s):  
Chenquan Hua ◽  
Chengjin Xie ◽  
Xuan Xu
Keyword(s):  
Edge Detection ◽  
Image Recognition ◽  
Measurement Errors ◽  
Ground Truth ◽  
Reconstruction Algorithm ◽  
Support Vector ◽  
Detection Model ◽  
Time Required ◽  
Fluid Levels

An image recognition technique is proposed for determining optimal neck levels for standard metal gauges, in the process of validating pipe provers. A camera-level follow-up control system was designed to achieve automated tracking of fluid levels by a camera, thereby preventing errors from inclined viewing angles. An orange background plate was placed behind the tube to reduce background interference, and highlight scale numbers/lines and concave meniscus. A segmentation algorithm, based on edge detection and K-means clustering, was used to segment indicator tubes and scales in the acquired images. The concave meniscus reconstruction algorithm and curve-fitting algorithm were proposed to better identify the lowest point of the meniscus. A characteristic edge detection model was used to identify centimeter-scale lines corresponding to the meniscus. A binary tree multiclass support vector machine (MCSVM) classifier was then used to identify scale numbers corresponding to scale lines and determine the optimal neck level for standard metal gauges. Experimental results showed that measurement errors were within ±0.1 mm compared to a ground truth acquired manually using Vernier calipers. The recognition time, including follow-up control, was less than 10 s, which is much lower than the switching time required between measuring individual tanks. This automated measurement approach for gauge neck levels can effectively reduce measurement times, decrease manmade errors in liquid level readings, and improve the efficiency of pipe prover validation.

Sign in / Sign up

Export Citation Format

Share Document