Research on Crack Detection Algorithm of Mining Car Baffle

2020 ◽  
Vol 13 ◽  
Author(s):  
Weiwei Li ◽  
Fanlei Yan
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Crack Detection ◽  
Low Cost ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Crack Identification ◽  
Actual Length ◽  
Computer Vision Technology ◽  
Sobel Edge Detection

Introduction: Image processing technology is widely used for crack detection. This technology is to build a data acquisition system and use computer vision technology for image analysis. Because of its simplicity in the processing, many of the image processing detection methods were proposed. It is relatively easy to deploy and has low cost. Method: The heterogeneity of the external light usually changes the authenticity of each target in the image, which will seriously cause the experiment to fail. At this time, the image needs to be processed by the gamma transform.Based on the analysis of the characteristics of the image of the mine car baffle, this paper improves the Gamma transform, and uses the improved Gamma transform to enhance the image. Result: We can conclude that the algorithm in this paper can accurately detect crack areas with an actual width greater than 1.2 mm, and the error between the detected crack length and the actual length is between (-2, 2) mm. In practice, this error is completely acceptable. Discussion: To compare the performance of a new crack detection method with existing methods, are used. The two most well-known traditional methods, Canny and Sobel edge detection, are selected. Although the Sobel edge detection provides some crack information. The texture of the surface of the mine cart baffle detected has caused great interference to the crack identification. Conclusion: If the cracks appearing on the mine car baffle are not found in time, they often cause accidents. Therefore, effective crack detection must be performed. If manual inspection is adopted for crack detection, it will be labor-intensive and easy to miss inspection. In order to reduce the labor of crack detection of mine cars and improve the accuracy of detection, this paper, based on the detection platform built, performs preprocessing, image enhancement, and convolution operations on the collected crack images of the mine car baffle.

scholarly journals Hardware Implementation of Sobel Edge Detection Algorithm

2020 ◽  
Vol 32 ◽  
pp. 03051
Author(s):  
Ankita Pujare ◽  
Priyanka Sawant ◽  
Hema Sharma ◽  
Khushboo Pichhode
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Feature Detection ◽  
Hardware Implementation ◽  
Software Tool ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Sobel Edge Detection ◽  
Image Edges

In the fields of image processing, feature detection, the edge detection is an important aspect. For detection of sharp changes in the properties of an image, edges are recognized as important factors which provides more information or data regarding the analysis of an image. In this work coding of various edge detection algorithms such as Sobel, Canny, etc. have been done on the MATLAB software, also this work is implemented on the FPGA Nexys 4 DDR board. The results are then displayed on a VGA screen. The implementation of this work using Verilog language of FPGA has been executed on Vivado 18.2 software tool.

Structural crack detection using deep learning–based fully convolutional networks

2019 ◽  
Vol 22 (16) ◽  
pp. 3412-3419 ◽  
Author(s):  
Xiao-Wei Ye ◽  
Tao Jin ◽  
Peng-Yu Chen
Keyword(s):  
Edge Detection ◽  
Structural Damage ◽  
Crack Detection ◽  
Training Data ◽  
Detection Methods ◽  
Crack Identification ◽  
Potential Threat ◽  
Online Data ◽  
Convolutional Network ◽  
Data Set

Cracks are a potential threat to the safety and endurance of civil infrastructures, and therefore, careful and regular structural crack inspection is needed during their long-term service periods. Many image-processing approaches have been developed for structural crack detection. However, like traditional edge detection algorithms, these methods are easily disturbed by the environmental effect. Convolutional neural networks are newly developed methods and have excellent performances in the image-classification tasks. This study proposes a fully convolutional network called Ci-Net for structural crack identification. Pixel-level labeled image training data are obtained from the online data set. Four indices are adopted to evaluate the performance of the trained Ci-Net. Crack images from an indoor concrete beam test are adopted for validation of its structural crack recognition capacity. The recognition results are also compared with those obtained by the edge detection methods. It indicates that Ci-Net exhibits a better performance over the edge detection methods in structural damage detection.

Research on Bolt Crack Detection Method Based on Image Processing

2013 ◽  
Vol 433-435 ◽  
pp. 426-429
Author(s):  
Jin Qiu Liu ◽  
Bing Fa Zhang ◽  
Yu Zeng Wang ◽  
Guang Ya Li ◽  
Jing Ru Han
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Image Recognition ◽  
Crack Detection ◽  
Detection Method ◽  
Image Acquisition ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Contact Detection ◽  
Fracture Identification

A method of non-contact detection of bolt fracture have serial steps as follows: First of all the required data is obtained through image acquisition, then through the edge detection, image recognition and other image processing on the image to get the bolt fracture identification results, finally the non-contact measurement bolt fracture is realized. Experiments show that bolt crack detection method based on image processing, compared with the traditional detection methods improve the efficiency of detection and improve the detection accuracy. The method for bolt crack detection is feasible.

Image Edge Detection Based on Neutrosophic Set Approach Combined with Chan–Vese Algorithm

2019 ◽  
Vol 33 (03) ◽  
pp. 1954008 ◽  
Author(s):  
Eser Sert ◽  
Ahmet Alkan
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Optimization Methods ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Neutrosophic Set ◽  
Neutrosophic Logic ◽  
Neutrosophic Statistics ◽  
Cv Model

Since edge detection is a field of study used by various disciplines, it is of vital importance to calculate it accuretly. In addition, an edge detection algorithm may be involved in many image processing phases. A recent and contemporary approach, neutrosophy is based on neutrosophic logic, neutrosophic probability, neutrosophic set and neutrosophic statistics. This method yields better results compared to various other optimization methods. Neutrosophic Set (NS) is based on the origin, nature and scope of neutralities. In NS, problems are separated into true, false and indeterminacy subsets. It helps solve indeterminate situations effectively. It has recently been used in the field of image processing as indeterminate situations are also encountered in this field. Chan–Vese (CV) model is one of the successful region-based segmentation methods. The present study proposes a new NS-based edge detection method using CV algorithm. The proposed method combines the philosophical view of NS with successful segmentation characteristics of CV model. Obtained edge detection results are compared with different edge detection methods. The performances of each method are analyzed by using Figure of Merit (FOM) and Peak Signal-To-Noise Ratio (PSNR). The results suggest that the proposed method displays a better performance assessment compared to the used well-known methods.

scholarly journals Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Quoc-Lam Nguyen
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Crack Detection ◽  
Median Filter ◽  
Area Under The Curve ◽  
Detection Methods ◽  
Flower Pollination Algorithm ◽  
Good Prediction ◽  
Concrete Wall ◽  
Promising Alternative

Crack detection is a crucial task in the periodic survey of high-rise buildings and infrastructure. Manual survey is notorious for low productivity. This study is aimed at establishing an image processing-based method for detecting cracks on concrete wall surfaces in an automatic manner. The Roberts, Prewitt, Canny, and Sobel algorithms are employed as the edge detection methods for revealing the crack textures appearing in concrete walls. The median filtering and object cleaning operations are used to enhance the image and facilitate the crack recognition outcome. Since the edge detectors, the median filter, and the object cleaning operation all require the appropriate selection of tuning parameters, this study relies on the differential flower pollination algorithm as a metaheuristic to optimize the image processing-based crack detection model. Experimental results point out that the newly constructed approach that employs the Prewitt algorithm can achieve a good prediction outcome with classification accuracy rate = 89.95% and area under the curve = 0.90. Therefore, the proposed metaheuristic optimized image processing approach can be a promising alternative for automatic recognition of cracks on the concrete wall surface.

Detection of Diabetic Retinopathy Using Sobel edge detection method in DIP

2012 ◽  
Vol 3 (7) ◽  
pp. 1-4
Author(s):  
Jyoti Patil ◽  
Dr. A. L. Chaudhari
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Edge Detection ◽  
Automated Analysis ◽  
Detection Algorithm ◽  
Sobel Edge Detection ◽  
Edge Detection Method ◽  
Image Edges ◽  
Processing Techniques ◽  
Automated Image Processing

Diabetic retinopathy, a complication of diabetes that occurs as a result of vascular changes in the retina, It is a major cause of loss of vision. Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel patterns in the retina. Image processing techniques can reduce the work of ophthalmologists and the tools used automatically locate the exudates. 0In this paper the process and knowledge of Digital Image Processing (DIP) is used. Automated analysis techniques for retinal images have been an important area of research for developing screening programmers. By using MATLAB for programming to develop the DIP tool for diagnosis of eye infection . Sobel edge detection algorithm is a method to find the edge pixels in an image. Edges are pixels which carry important information in an image. Thus sobel method is best technique for features are extended & used to classify the pixels in the patch into vessel and non vessel

scholarly journals Low Complexity Lane Detection Methods for Light Photometry System

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1665
Author(s):  
Jakub Suder ◽  
Kacper Podbucki ◽  
Tomasz Marciniak ◽  
Adam Dąbrowski
Keyword(s):  
Edge Detection ◽  
Optimal Path ◽  
Color Space ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Detection Methods ◽  
Raspberry Pi ◽  
Line Detection ◽  
Hsv Color Space

The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color space. The developed solutions were tuned and tested with the use of embedded devices such as Raspberry Pi 4B or NVIDIA Jetson Nano.

scholarly journals Identification of Bone Fragmentation in X-Ray Images using Contour Detection Algorithm

2019 ◽  
Vol 8 (9) ◽  
pp. 2121-2124
Keyword(s):  
Edge Detection ◽  
Bone Fracture ◽  
Detection Algorithm ◽  
Contour Detection ◽  
Crack Identification ◽  
Fracture Detection ◽  
Canny Edge Detection ◽  
X Ray ◽  
Base Paper ◽  
Canny Edge

The crack can occur in any bone ofour body. Broken bone is a bone condition that endured a breakdown of bone trustworthiness. The Fracture can't recognize effortlessly by the bare eye, so it is found in the x-beam images. The motivation behind this task is to find the precise territory where the bone fracture happens utilizing X-Ray Bone Fracture Detection by Canny Edge Detection Method. Shrewd Edge Detection technique is an ideal edge identification calculation on deciding the finish of a line with alterable limit and less error rate. The reproduction results have indicated how canny edge detection can help decide area of breaks in x-beam images. In the base paper, the cracked bit is chosen physically to defeat this downside, the proposed technique identify the bone fracture consequently and furthermore it quantifies the parameter like length of the crack, profundity of the fracture and the situation of the crack as for even and vertical pivot. The outcome demonstrates that the proposed technique for crack identification is better. The outcomes demonstrate that calculation is 91% exact and effective

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