Edge-preserving Filtering and Fuzzy Image Enhancement in Depth Images Captured by Realsense Cameras in Robotic Applications

Tadić, Vladimir; Odry, Ákos; Burkus, Ervin; Kecskés, István; Király, Zoltán; Ódry, Péter

doi:10.4316/aece.2020.03010

Cited by 6 publications

(14 citation statements)

References 21 publications

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“…Although a wealth of in the field of aircraft fatigue detection at home and abroad, aircraft landing gear fatigue accidents caused by aircraft landing gear crack failures still occur from time to time. The main reasons are the high cost and long cycle of the landing gear fatigue test, which makes it difficult to apply the theoretical research results in practice [8]. Therefore, by designing image crack detection methods, the safety factor of the landing gear products is improved.…”

Section: The Overall Framework For Intelligent Detection Of Internal Cracks In Aircraft Landing Gear Imagesmentioning

confidence: 99%

Intelligent Detection Method for Internal Cracks in Aircraft Landing Gear Images under Multimedia Processing

Luo

Zhang

2021

Symmetry

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In view of the lack of image enhancement processing in the traditional methods in image preprocessing, which leads to a long detection time for internal cracks in the image and poor visual effects, an intelligent detection method for internal cracks in aircraft landing gear images under multimedia processing is proposed. A spatial index structure is established based on the multimedia database, and the aircraft landing gear images in the structure are enhanced and denoised. Image segmentation is performed according to the preprocessing results, the crack foreground and the road surface background in the image are separated, and the threshold value of each image is calculated. The threshold segmentation result is used to distinguish which pixels are the areas where the cracks may exist and which pixels belong to the image background, and the judgment result realizes crack detection. The experimental results show that the crack detection time of the proposed method is shorter, the visual effect of the detection results is better, and the average of the expert score reaches 93.6 points, which verifies the effectiveness of the proposed method from both the subjective and objective aspects.

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Section: The Overall Framework For Intelligent Detection Of Internal Cracks In Aircraft Landing Gear Imagesmentioning

confidence: 99%

Intelligent Detection Method for Internal Cracks in Aircraft Landing Gear Images under Multimedia Processing

Luo

Zhang

2021

Symmetry

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“…This strategy is quite new in commercial wall painting via robot construction. The basic hardware for the vision system is a Intel RealSense D435 depth camera [1][2][3][4], which is replaced by a StereoLabs ZED depth camera [5]. The goal is to develop a robust and simple computer vision algorithm to detect and extract mainly rectangular windows and large rectangular obstacles on the wall near the window area using depth images recorded with a stereo camera, as well as notifying the controlling system [1].…”

Section: Introductionmentioning

confidence: 99%

“…In previous research, the robot's vision system was developed using the real-time appearance-based mapping (RTAB-Map) algorithm and the random sample consensus (RANSAC) algorithm [1]. The main reason for using the mentioned procedures is that the RealSense depth camera yields the best depth measuring results from a distance of approximately 60-70 cm [1][2][3][4]. Unfortunately, the size of the captured wall surface at this distance is only approximately 85 cm × 63 cm.…”

Section: Introductionmentioning

confidence: 99%

“…The ZED depth sensor is among the best commercial depth sensors on the market [5][6][7][8], and it measures the depth with very high accuracy. It has numerous advantages over RealSense depth cameras, such as a better depth resolution, better depth accuracy, wider field of view, higher red, green and blue (RGB) resolution, and wider distance range [1][2][3][4][5][6][7][8]. However, its price is many times higher.…”

Section: Introductionmentioning

confidence: 99%

“…The RGBD camera was included in the ROS framework via its ROS wrapper. This wrapper enabled obtaining the realtime measurements on multiple ROS topics [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of the ZED Depth Sensor for Painting Robot Vision System Development

et al. 2021

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This paper presents the use of the ZED depth sensor in a robot-based painting application. The use of a stereo depth sensor is a very important factor in robotic applications, since it is both the initial and the essential step in a sequence of robotic operations, where the goal is to detect and extract the useful surface and objects or the obstacle on a wall that is not intended for painting. The ZED depth sensor was used for surface recording and navigation of our painting robot. Later, wall extraction was performed using simple image processing and morphological operations in a surface extraction algorithm. The client's instruction was to use well-known, simple, and proven image processing operations in the algorithm to ensure both reliable and smooth operation of the robot's vision system in an industrial environment. The experiments showed that the developed algorithm detects and extracts the wall successfully under various depth measurement conditions.

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Overview of modulation techniques for spatially structured-light 3D imaging

Yang,

2024

Optics & Laser Technology

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Edge-preserving Filtering and Fuzzy Image Enhancement in Depth Images Captured by Realsense Cameras in Robotic Applications

Cited by 6 publications

References 21 publications

Intelligent Detection Method for Internal Cracks in Aircraft Landing Gear Images under Multimedia Processing

Intelligent Detection Method for Internal Cracks in Aircraft Landing Gear Images under Multimedia Processing

Application of the ZED Depth Sensor for Painting Robot Vision System Development

Overview of modulation techniques for spatially structured-light 3D imaging

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