Horizon detection in maritime images using scene parsing network

C.Y. Jeong; H.S. Yang; K.D. Moon

doi:10.1049/el.2018.0989

Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions

2020 IEEE Winter Conference on Applications of Computer Vision (WACV) ◽

10.1109/wacv45572.2020.9093449 ◽

2020 ◽

Author(s):

Kashyap Chitta ◽

Jose M. Alvarez ◽

Martial Hebert

Keyword(s):

Scene Parsing

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MFFENet: Multiscale Feature Fusion and Enhancement Network for RGBThermal Urban Road Scene Parsing

IEEE Transactions on Multimedia ◽

10.1109/tmm.2021.3086618 ◽

2021 ◽

pp. 1-1

Author(s):

Wujie Zhou ◽

Xinyang Lin ◽

Jingsheng Lei ◽

Lu Yu ◽

Jeng-Neng Hwang

Keyword(s):

Feature Fusion ◽

Urban Road ◽

Scene Parsing

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Attitude estimation using horizon detection in thermal images

International Journal of Micro Air Vehicles ◽

10.1177/1756829318804761 ◽

2018 ◽

Vol 10 (4) ◽

pp. 352-361 ◽

Cited By ~ 3

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.

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