Robust Horizon Detection Using Segmentation for UAV Applications

2012 ◽  
Author(s):  
Nasim Sepehri Boroujeni ◽  
S. Ali Etemad ◽  
Anthony Whitehead
Keyword(s):  
Horizon Detection

scholarly journals Attitude estimation using horizon detection in thermal images

2018 ◽  
Vol 10 (4) ◽  
pp. 352-361 ◽  
Author(s):  
Adrian Carrio ◽  
Hriday Bavle ◽  
Pascual Campoy
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Infinite Horizon ◽  
Attitude Estimation ◽  
Thermal Image ◽  
Image Sensors ◽  
Image Resolution ◽  
Estimation Methods ◽  
Operation Conditions ◽  
Aerial Vehicles ◽  
Horizon Detection

The lack of redundant attitude sensors represents a considerable yet common vulnerability in many low-cost unmanned aerial vehicles. In addition to the use of attitude sensors, exploiting the horizon as a visual reference for attitude control is part of human pilots’ training. For this reason, and given the desirable properties of image sensors, quite a lot of research has been conducted proposing the use of vision sensors for horizon detection in order to obtain redundant attitude estimation onboard unmanned aerial vehicles. However, atmospheric and illumination conditions may hinder the operability of visible light image sensors, or even make their use impractical, such as during the night. Thermal infrared image sensors have a much wider range of operation conditions and their price has greatly decreased during the last years, becoming an alternative to visible spectrum sensors in certain operation scenarios. In this paper, two attitude estimation methods are proposed. The first method consists of a novel approach to estimate the line that best fits the horizon in a thermal image. The resulting line is then used to estimate the pitch and roll angles using an infinite horizon line model. The second method uses deep learning to predict attitude angles using raw pixel intensities from a thermal image. For this, a novel Convolutional Neural Network architecture has been trained using measurements from an inertial navigation system. Both methods presented are proven to be valid for redundant attitude estimation, providing RMS errors below 1.7° and running at up to 48 Hz, depending on the chosen method, the input image resolution and the available computational capabilities.

scholarly journals Method of the horizon detection from electro-optical sensors under maritime environment

2018 ◽  
pp. 26-32
Author(s):  
С.П. Євсеєв ◽  
О.В. Шматко ◽  
Лян Дун ◽  
Є.В. Бабенко
Keyword(s):  
Optical Sensors ◽  
Horizon Detection ◽  
Maritime Environment

Vision Based Horizon Detection for UAV Navigation

2018 ◽  
pp. 181-189
Author(s):  
Stavros Timotheatos ◽  
Stylianos Piperakis ◽  
Antonis Argyros ◽  
Panos Trahanias
Keyword(s):  
Horizon Detection ◽  
Uav Navigation

Anisotropic imaging in the dragonfly median ocellus: a matched filter for horizon detection

2002 ◽  
Vol 188 (6) ◽  
pp. 455-467 ◽  
Author(s):  
Stange G. ◽  
Stowe S. ◽  
Chahl J. ◽  
Massaro A.
Keyword(s):  
Matched Filter ◽  
Median Ocellus ◽  
Horizon Detection

scholarly journals Fast horizon detection in maritime images using region-of-interest

2018 ◽  
Vol 14 (7) ◽  
pp. 155014771879075 ◽  
Author(s):  
Chi Yoon Jeong ◽  
Hyun S Yang ◽  
KyeongDeok Moon
Keyword(s):  
Edge Detection ◽  
State Of The Art ◽  
Region Of Interest ◽  
Least Square ◽  
Fast Method ◽  
Multi Scale ◽  
Art Methods ◽  
Horizon Line ◽  
Horizon Detection ◽  
Interest Detection

In this article, we propose a fast method for detecting the horizon line in maritime scenarios by combining a multi-scale approach and region-of-interest detection. Recently, several methods that adopt a multi-scale approach have been proposed, because edge detection at a single is insufficient to detect all edges of various sizes. However, these methods suffer from high processing times, requiring tens of seconds to complete horizon detection. Moreover, the resolution of images captured from cameras mounted on vessels is increasing, which reduces processing speed. Using the region-of-interest is an efficient way of reducing the amount of processing information required. Thus, we explore a way to efficiently use the region-of-interest for horizon detection. The proposed method first detects the region-of-interest using a property of maritime scenes and then multi-scale edge detection is performed for edge extraction at each scale. The results are then combined to produce a single edge map. Then, Hough transform and a least-square method are sequentially used to estimate the horizon line accurately. We compared the performance of the proposed method with state-of-the-art methods using two publicly available databases, namely, Singapore Marine Dataset and buoy dataset. Experimental results show that the proposed method for region-of-interest detection reduces the processing time of horizon detection, and the accuracy with which the proposed method can identify the horizon is superior to that of state-of-the-art methods.

Horizon detection in maritime images using scene parsing network

2018 ◽  
Vol 54 (12) ◽  
pp. 760-762 ◽  
Author(s):  
C.Y. Jeong ◽  
H.S. Yang ◽  
K.D. Moon
Keyword(s):  
Scene Parsing ◽  
Horizon Detection
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