Automatic defect identification technology of digital image of pipeline weld
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Abstract:
Digital image of pipeline weld is an important basis for the reliability management of pipeline welds. However, the error rate of artificial discrimination is high. In order to increase the defect identification accuracy of digital image of pipeline weld, we adopted several methods (e.g. multiple edge detection, detection channel and threshold segmentation) to carry out image processing on the image defects of pipeline welds. Then, a defect characteristic database on the digital images of pipeline welds was constructed, including grayscale difference, equivalent area (S/C), circularity, entropy, correlation and other parameters. Furthermore, a multi-classifier construction (SVM) model was established. Thus, the classification and evaluation on the defects in the digital images of pipeline welds were realized. Finally, an automatic defect identification software for digital image of pipeline weld was developed and verified on site. And the following research results were obtained. First, after image processing, the edge detection results obtained by Canny and other algorithms are satisfactory when there is no noise. In the case of noise, however, pseudo-edge emerges in the detection results. In this case, the automatic threshold selection method shall be adopted to detect the image edge to obtain the rational threshold. Second, there are 14 parameters in the defect characteristic database, including shape characteristic, lamination characteristic and image length pixel. Third, by virtue of the SVM classification model, the shape characteristics of each type of defect can be clarified, and the defect characteristics can be identified, such as crack, slag inclusion, air hole, incomplete penetration, non-fusion and strip. Based on field application, the following results were obtained. First, this automatic defect identification technology is applicable to quality identification and evaluation of various defects in pipeline welds. Second, its identification accuracy is higher than 90%. Third, by virtue of this technology, automatic defect identification and evaluation of digital image of pipeline weld is realized. In conclusion, these research results help to ensure the safe operation of pipelines.Keywords:
Canny edge detector
In computer vision and image processing edge detection is an important topic. In what follows, a simple edge detection and fast calculation method using fuzzy rules is presented. The fuzzy rule system is designed to model edge continuity criteria. To adjust parameters the maximum entropy principle is used for. We also discuss the related issues in designing fuzzy edge detectors. Every step of evolution of the detector we compare it with popular edge detectors: Canny edge detector. The proposed fuzzy edge detector does not need parameter setting as Canny's; also it can preserve an appropriate detection in details. High level noise does not affect the detection, in addition it can work well under situations that other edge detectors cannot. The filtering process is unnecessary because the detector efficiently extracts edges in images corrupted by noise without requiring it. The experimental results demonstrate the superiority of the proposed method.
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Deriche edge detector
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This paper presents the edge detection and level of pixels to proceed further. Edge Detection is a kind of image- segmentation process and image-segmentation is one of the pre-processing steps in Image Processing. It is used in many places such as the detection of an object lying on the line of sight, the extraction of damaged videos, enhancing the image so it can be recognized well by adjusting the light and dark areas etc. There are variety of image detection algorithms, for example, Canny Edge Detection, Prewitt Edge Detection, Sobel Edge Detection, Roberts Edge Detection, etc. but this research paper will focus on Canny Edge Detection algorithm as it overcomes the shortcomings of other very popular edge detection algorithm called as Sobel Edge Detection algorithm and how we can use the technique for precise detection of obstacles as it is a very important part for our robot system should include.
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Edges of an image are considered a type of crucial information that can be extracted by applying detectors with different methodology. Edge detection is a basic and important subject in computer vision and image processing In this Paper we discuss several Digital Image Processing Techniques applied in edge feature extraction. Firstly, Linear filtering of Image is done is used to remove noises from the image collected. Secondly, some edge detection operators such as Sobel, Log edge detection, canny edge detection are analyzed and then according to the simulation results, the advantages and disadvantages of these edge detection operators are compared. It is shown that the canny operator can obtain better edge feature. Finally, Edge detection is applied to find crack in a bone of a hand. After experimentation, edge detection method proposed in this paper is Feasible.
Deriche edge detector
Canny edge detector
Sobel operator
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This paper is concerned with the study of various edge detection techniques like Sobel, Prewitt, Robert's Cross, Zerocross and Canny on various road images to detect edges and to extract some road features. This paper also outlines definition of edge detection, different types of edges, steps in edge detection. A comparison between these edge detectors have been examined and numerical and visual results are outperformed. It has been observed that the Canny's edge detector yields better results than all other edge
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CANNY operator had widely usage for edge detection; however it also had certain deficiencies. So the traditional CANNY operator about this is improved and puts forward a kind of new algorithm used for image edge detection. Compared improved algorithm with traditional algorithm for edge detection, simulations shows that new algorithm is more effective for image edge detection and the clearer detection result is obtained.
Deriche edge detector
Canny edge detector
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For single edge detection methods causing important and weak gradient change edge missing problems, this paper adopts the method of combining global with local edge detection to extract edge. The global edge detection can obtain the whole edge, which uses adaptive smooth filter algorithm based on Canny operator. Compared with effect of edge detection from the Canny operator and Sobel operator, the edge from improved Canny operator is the most complete and rich, do not contain false edge. To the whole detection failed to get the edge, the paper selects local area detection method for edge extraction. Local edge detection which uses distance weighted average method based on k-average method can overcome the impact of outliers on clustering effectively. Complete skull image edge is got through edge detection method that combines global with local. Compared with the Canny edge detection method, this algorithm can extract image edge effectively, and have the powerful anti-noise ability.
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: Edge detection method has been utilized to find the boundaries of things within the input images provided. Edge detection method introduces to the operation of discovering and finding the sharp disconnections or the disjointedness in the brightness. Edge detection process reduces the noises in the images. Canny algorithm is one of the excellent edge detection methods or the algorithms which is extensively used in the image processing field. As techniques like computer vision does the classification and recognition of objects in an input image, edge detection is an amazing tool which can be used for this purpose. The main focus in this paper is to learn edge detection process by comparing different types of edge detection operators also execute canny’s edge detection operator or the technique. Edge detection is fundamentally an image segmentation method, divides structural domain, and then on which the image is identified, into valued regions or parts. Edges characterize borders and are therefore a problem of basic importance in image processing. The main focus is to learn edge detection process based on different operators and execute canny’s edge detection method. Here we have compared all other other algorithms with the canny edge algorithm on the output efficiency.
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Edge is the most basic features of the image, including images which are used to identify useful information, edge detection is the basical and important issue of digital image processing. The article specifically examines five kinds of classical edge detection operator which are commonly used and least squares support vector machines which extract edge detection operator, and the image processing results are compared by Matlab. Gradient operator is simple and effective, LOG algorithm and Canny edge detector can produce smaller edge. Least squares support vector machine which combine image of the gradient and zero-crossing information can get better performance than the Canny method by selecting a certain parameter conditions.
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Identification of image edges using edge detection is done to obtain images that are sharp and clear. The selection of the edge detection algorithm will affect the result. Canny operators have an advantage compared to other edge detection operators because of their ability to detect not only strong edges but also weak edges. Until now, Canny edge detection has been done using classical computing where data are expressed in bits, 0 or 1. This paper proposes the identification of image edges using a quantum Canny edge detection algorithm, where data are expressed in the form of quantum bits (qubits). Besides 0 or 1, a value can also be 0 and 1 simultaneously so there will be many more possible values that can be obtained. There are three stages in the proposed method, namely the input image stage, the preprocessing stage, and the quantum edge detection stage. Visually, the results show that quantum Canny edge detection can detect more edges compared to classic Canny edge detection, with an average increase of 4.05%.
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Edge detection is an important field in image processing. Edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection techniques. Various image edge detection techniques are introduced. These techniques are compared by using MATLAB7.0. The qualities of these techniques are elaborated. The results show that Canny edge detection techniques is better than others.
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Image gradient
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