Water detection from downwash-induced optical flow for a multirotor UAV

Pombeiro, Ricardo; Mendonca, Ricardo; Rodrigues, Paulo M. M.; Marques, Francisco; Lourenço, André; Pinto, Eduardo; Santana, Pedro; Barata, José

doi:10.23919/oceans.2015.7404458

Cited by 10 publications

(15 citation statements)

References 14 publications

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“…Thus, the UAVs must be able to capture, process and analyze environment images in order to classify the terrain type under its flight area. Until now, only two recent publications [17,18] were known to use the concept of downwash effect for terrain classification. Ricardo Pombeiro [17] uses the optical flow concept to extract the dynamic part of an image using the Lukas Kanade method [17], from an onboard RGB camera.…”

Section: Introductionmentioning

confidence: 99%

“…The second most recent work also using the downwash effect to classify terrain types, beyond the use of dynamic properties, also takes advantage of static properties of an image. Unlike in [17], the work presented in [18] not only can identify water, but also sand and vegetation terrains and can do so in near real-time (in less than 30 ms). To classify the terrain type, the system from [18] uses the gabor concept in order to extract the static terrain texture; the optical flow concept, is used under the Farneback algorithm, which obtains the dynamic terrain properties.…”

Section: Introductionmentioning

confidence: 99%

“…To classify the terrain type, the system from [18] uses the gabor concept in order to extract the static terrain texture; the optical flow concept, is used under the Farneback algorithm, which obtains the dynamic terrain properties. However, the systems from [17] and [18] only identify one type of terrain per frame, which clearly becomes a disadvantage when there are several types of terrain in a single frame.…”

Section: Introductionmentioning

confidence: 99%

“…The previously mentioned algorithms suffer from a high sensitivity to changes in the environment, mainly due to changes in brightness, color and texture. Recent works [17,18] proposed the use of downwash effect to overcome these limitations.The downwash effect caused by UAV propellers, shown in Figure 1, might have a major impact on terrain classification, because each terrain, when subject to this effect, present different behaviours. Take the example of water-type terrains, where there is no color variation, i.e, no motion presented, these are wrongly classified by algorithms prepared to evaluate static textures, as shown in [2][3][4][5].…”

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Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

et al. 2019

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The ability to precisely classify different types of terrain is extremely important for Unmanned Aerial Vehicles (UAVs). There are multiple situations in which terrain classification is fundamental for achieving a UAV's mission success, such as emergency landing, aerial mapping, decision making, and cooperation between UAVs in autonomous navigation. Previous research works describe different terrain classification approaches mainly using static features from RGB images taken onboard UAVs. In these works, the terrain is classified from each image taken as a whole, not divided into blocks; this approach has an obvious drawback when applied to images with multiple terrain types. This paper proposes a robust computer vision system to classify terrain types using three main algorithms, which extract features from UAV's downwash effect: Static textures-Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM) and Dynamic textures-Optical Flow method. This system has been fully implemented using the OpenCV library, and the GLCM algorithm has also been partially specified in a Hardware Description Language (VHDL) and implemented in a Field Programmable Gate Array (FPGA)-based platform. In addition to these feature extraction algorithms, a neural network was designed with the aim of classifying the terrain into one of four classes. Lastly, in order to store and access all the classified terrain information, a dynamic map, with this information was generated. The system was validated using videos acquired onboard a UAV with an RGB camera.Several methods for terrain classification have been proposed in different works. Texture algorithms, such as those proposed in [2][3][4][5], have been widely recommended to emphasize the high and low frequencies of the images, supporting image classification. Other algorithms use color information to classify terrains, such as presented in [6], which is able to distinguish four different terrain types within an image. During this process, each channel's pixel is divided by the square root of its own three channels intensity. The final result will emphasize the color that most represents the terrain type (eg, blue for water). Additionally, frequency domain [7,8], segmentation [6,9,10], bayesian network [11], and Hyperspectal Images [12] can also be used in terrain classification.Other types of sensors such as LiDAR [13][14][15][16] can complement the classification decision. Algorithms that use laser scanners proved to be qualified to accurately distinguish between water and non-water terrains [13][14][15][16]. However, shallow water terrains increase the decision error due to laser reflection, which leads to a misclassification as non-water terrain.Although prior research work has proposed many good solutions for terrain classification, there is still a gap regarding the study of dynamic terrain. The previously mentioned algorithms suffer from a high sensitivity to changes in the environment, mainly due to changes in brightness, color and texture. Recent works [17,18] ...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

et al. 2019

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UAV Downwash-Based Terrain Classification Using Wiener-Khinchin and EMD Filters

Matos-Carvalho

Mora

Rato

et al. 2019

IFIP Advances in Information and Communication Technology

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Knowing how to identify terrain types is especially important in the autonomous navigation, mapping, decision making and detect landings areas. A recent area is in cooperation and improvement of autonomous behavior between robots. For example, an unmanned aerial vehicle (UAV) is used to identify a possible landing area or used in cooperation with other robots to navigate in unknown terrains. This paper presents a computer vision algorithm capable of identifying the terrain type where the UAV is flying, using its rotors' downwash effect. The algorithm is a fusion between the frequency Wiener-Khinchin adapted and spatial Empirical Mode Decomposition (EMD) domains. In order to increase certainty in terrain identification, machine learning is also used. The system is validated using videos acquired onboard of a UAV with an RGB camera.

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Drift potential and coverage ratio analysis of an air induction nozzle under an agricultural drone with various operating condition; an indoor test

Alidoost Dafsari,

Yu,

et al. 2024

Computers and Electronics in Agriculture

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Water detection from downwash-induced optical flow for a multirotor UAV

Cited by 10 publications

References 14 publications

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

UAV Downwash-Based Terrain Classification Using Wiener-Khinchin and EMD Filters

Drift potential and coverage ratio analysis of an air induction nozzle under an agricultural drone with various operating condition; an indoor test

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