A Novel Method of the Brillouin Gain Spectrum Recognition Using Enhanced Sobel Operators Based on BOTDA System

Li, Jiaqi; Zhou, Weinan; Zhang, Youdi; Dong, Wei; Zhang, Xindong

doi:10.1109/jsen.2019.2899034

Cited by 12 publications

(3 citation statements)

References 23 publications

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“…The first-order differential operators mainly include a Roberts operator [ 27 ], Sobel operator [ 28 ], Prewitt operator [ 29 ], and Canny operator [ 30 ]. The Roberts operator has high edge positioning accuracy but is sensitive to noise.…”

Section: Methodsmentioning

confidence: 99%

Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain

Chen

Liu

et al. 2020

Sensors

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Obstacle detection is one of the essential capabilities for autonomous robots operated on unstructured terrain. In this paper, a novel laser-based approach is proposed for obstacle detection by autonomous robots, in which the Sobel operator is deployed in the edge-detection process of 3D laser point clouds. The point clouds of unstructured terrain are filtered by VoxelGrid, and then processed by the Gaussian kernel function to obtain the edge features of obstacles. The Euclidean clustering algorithm is optimized by super-voxel in order to cluster the point clouds of each obstacle. The characteristics of the obstacles are recognized by the Levenberg–Marquardt back-propagation (LM-BP) neural network. The algorithm proposed in this paper is a post-processing algorithm based on the reconstructed point cloud. Experiments are conducted by using both the existing datasets and real unstructured terrain point cloud reconstructed by an all-terrain robot to demonstrate the feasibility and performance of the proposed approach.

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Section: Methodsmentioning

confidence: 99%

Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain

Chen

Liu

et al. 2020

Sensors

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“…In the previous section, the BGS of the BOTDR sensing system is regarded as an image, and the most prominent edge in the figure is the location of the roof-like BFS. Therefore, the edge detection technique can extract the brillouin frequency shift features of the BGS image [29,30]. The convolution matrices for the X and Y directions are as follows:…”

Section: Sobel-based Botdr Brillouin Gain Spectrum Frequency Shift Fe...mentioning

confidence: 99%

Fast Feature Extraction Method for Brillouin Scattering Spectrum of OPGW Optical Cable Based on BOTDR

Chen,

2023

Sensors

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Brillouin optical time domain reflectometry (BOTDR) detects fiber temperature and strain data and represents one of the most critical ways of identifying abnormal conditions such as ice coverage and lightning strikes on optical fiber composite overhead ground wire (OPGW) cable. Existing BOTDR extracts brillouin frequency shift (BFS) features with cumulative averaging and curve fitting. BFS feature extraction is slow for long-distance measurements, making realizing real-time measurements on fiber optic cables challenging. We propose a fast feature extraction method for block matching and 3D filtering (BM3D) + Sobel brillouin scattering spectroscopy (BGS). BM3D takes the advantage of non-local means (NLM) and wavelet denoising (WD) and utilizes the spatial-domain non-local principle to enhance the denoising in the transform domain. The global filtering capability of BM3D is utilized to filter out the low cumulative average BGS noise and the BFS feature extraction is completed using Sobel edge detection. Simulation verifies the feasibility of the algorithm, and the proposed method is embedded in BOTDR to measure 30 km of actual OPGW line. The experimental results show that under the same conditions, the processing time of this method is reduced by 37 times compared to that with the 50,000-time cumulative averaging + levenberg marquardt (LM) algorithm without severe distortion of the reference resolution. The method improves the sensor demodulation speed by using image processing technology without changing the existing hardware equipment, which is expected to be widely used in the new generation of BOTDR.

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“…With the development of computer hardware technology, the resolution of the display is getting higher and higher, which will inevitably cause the reduction of the edge grayscale change band [9,10]. The edges of the image are always generated in different scales, forming different edges, and this information is unknown before image processing.…”

Section: Introductionmentioning

confidence: 99%

Image Edge Recognition of Virtual Reality Scene Based on Multi-Operator Dynamic Weight Detection

Liu

Chen

et al. 2020

IEEE Access

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Image edge detection in virtual reality scenes is one of the most important technologies in the field of image processing and computer vision. It occupies an important position in the image processing system and is a key factor that affects the performance of the entire system. The quality of the algorithm directly affects the performance of the computer's visual system. This paper briefly describes the basic theoretical methods of image edge detection. The basic theory and processing flow of image edge detection are analyzed, and several commonly used edge detection preprocessing methods and their basic principles are elaborated in detail. The existing traditional edge detection methods are briefly introduced, and the traditional edge detection methods are compared. This paper studies the adaptive multi-scale edge detection method based on Canny algorithm, and through theoretical analysis, compares the advantages and disadvantages of various algorithms in image edge detection. In the process of acquiring image edges, the local maximum value of the modulus is calculated along the gradient direction and the threshold is adaptively selected based on the image blocking principle. Comparing with the traditional modulus maximum edge detection method, this algorithm overcomes the contradiction that can suppress the interference of noise and obtain better edge detection effect to a certain extent. Experiments show that the method in this paper not only suppresses the interference of impulsive noise to the image edge detection, but also greatly reduces the possibility of false edges, and obtains a satisfactory accurate binary edge image. INDEX TERMS edge detection; multiple operators; adaptive dynamic weights; virtual reality scenes.

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A Novel Method of the Brillouin Gain Spectrum Recognition Using Enhanced Sobel Operators Based on BOTDA System

Cited by 12 publications

References 23 publications

Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain

Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain

Fast Feature Extraction Method for Brillouin Scattering Spectrum of OPGW Optical Cable Based on BOTDR

Image Edge Recognition of Virtual Reality Scene Based on Multi-Operator Dynamic Weight Detection

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