Railway Fastener Defects Detection under Various Illumination Conditions using Fuzzy C-Means Part Model

2020 ◽  
pp. 036119812097718
Author(s):  
Biao He ◽  
Jianqiao Luo ◽  
Yang Ou ◽  
Ying Xiong ◽  
Bailin Li
Keyword(s):  
Defect Detection ◽  
Clustering Algorithm ◽  
Railway Track ◽  
Histogram Of Oriented Gradients ◽  
Safe Operation ◽  
Fuzzy C Means ◽  
Illumination Changes ◽  
Fuzzy C Means Clustering ◽  
Routine Inspection ◽  
Part Model

To ensure the safe operation of railways, computer vision and pattern recognition technology have been gradually applied to the routine inspection of railway track infrastructure. Rails are fixed to sleepers by railway fasteners, which are important components in railway track systems, and completely missing and partially worn railway fasteners may cause major accidents and train derailments. Because the acquisition of track images is carried out in the real world at any time of the day, the acquired track images have large illumination changes, and the fasteners in the images have slight deformations. To solve these problems, a fuzzy c-means part model (FCMPM) is proposed in this paper. The fastener part model is divided according to the fastener shape and solved by the fuzzy c-means clustering algorithm using the simplified and improved histogram of oriented gradients as the low-level feature. The part score is calculated based on the part’s deformation and then is seamlessly incorporated with cascade detection to determine whether the fastener has defects or not. The experimental results from the fastener defect detection show that the proposed FCMPM algorithm achieves good performance when analysing the collected fastener images and can meet the requirements of fastener defect detection for actual railway lines.

An Infrared Thermal Image Processing Framework for Defect Detection of a Metal Part with Rough Surface

2012 ◽  
Vol 229-231 ◽  
pp. 1356-1360
Author(s):  
Jing Xie ◽  
Chang Hang Xu ◽  
Guo Ming Chen
Keyword(s):  
Image Processing ◽  
Rough Surface ◽  
Similarity Measure ◽  
Defect Detection ◽  
Clustering Algorithm ◽  
Thermal Image ◽  
Metal Part ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Processing Framework

We propose an infrared thermal image processing framework based on a modified fuzzy c-means clustering algorithm with revised similarity measure in this paper. The framework can realize the defect detection of a metal part with rough surface. Firstly, a comprehensive method is used to preprocess infrared thermal image. Secondly, the preprocessed image is segmented using modified fuzzy c-means clustering algorithm with revised similarity measure. Finally, taking the average gray level of each cluster in the original gray scale image as a feature, defect cluster is recognized. Experimental result shows that the proposed framework has very promising performance and can obtain precise information of defects on a metal part with rough surface.

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

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