Journal ArticleDOI
Automatic Visual Detection System of Railway Surface Defects With Curvature Filter and Improved Gaussian Mixture Model
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TLDR
An automatic railway visual detection system (RVDS) for surface defects and focuses on several key issues of RVDS, which enables identification and segmentation of the defects from rail surface, achieving detection performance with 92% precision and 88.8% recall rate on average.Abstract:
Rails are among the most important components of railway transportation, and real-time defects detection of the railway is an important and challenging task because of intensity inhomogeneity, low contrast, and noise. This paper presents an automatic railway visual detection system (RVDS) for surface defects and focuses on several key issues of RVDS. First, in view of challenges such as complex condition and orbital reflectance inequality, we put forward a region-of-interest detection region extraction algorithm by vertical projection and gray contrast algorithm. In addition, a curvature filter equipped with implicit computing and surface preserving power is studied to eliminate noise and keep only the details. Then, an improved fast and robust Gaussian mixture model based on Markov random field is established for accurate and rapid surface defect segmentation. Additionally, an expectation–maximization algorithm is applied to optimize the parameters. The experimental results demonstrate that the proposed method performs well with both noisy and railway images, which enables identification and segmentation of the defects from rail surface, achieving detection performance with 92% precision and 88.8% recall rate on average, and is robust compared with the related well-established approaches.read more
Citations
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Journal ArticleDOI
Automated defect inspection system for metal surfaces based on deep learning and data augmentation
TL;DR: A new convolutional variational autoencoder (CVAE) and deep CNN-based defect classification algorithm to solve the problem of automatic defect inspection in the metal manufacturing industry.
Journal ArticleDOI
Semi-supervised defect classification of steel surface based on multi-training and generative adversarial network
TL;DR: A semi-supervised learning (SSL) defect classification approach based on multi-training of two different networks: a categorized generative adversarial network (GAN) and a residual network and the GAN to generate a large number of unlabeled samples is proposed.
Journal ArticleDOI
A Surface Defect Detection Framework for Glass Bottle Bottom Using Visual Attention Model and Wavelet Transform
TL;DR: A new localization method named ERSCD, which combines least-squares circle detection and entropy rate superpixel (ERS) with an improved randomized circle detection, is proposed to accurately obtain the region of interest (ROI) of the bottle bottom.
Journal ArticleDOI
Triplet-Graph Reasoning Network for Few-Shot Metal Generic Surface Defect Segmentation
TL;DR: Wang et al. as discussed by the authors proposed a triplet-graph reasoning network (TGRNet) for metal surface defect segmentation, which includes triplet encoder and trip loss to segment background and defect area, respectively.
Journal ArticleDOI
FPCB Surface Defect Detection: A Decoupled Two-Stage Object Detection Framework
TL;DR: Wang et al. as discussed by the authors proposed a decoupled two-stage object detection framework based on convolutional neural networks (CNNs), wherein the localization task and the classification task are decouple through two specific modules, namely multi-hierarchical aggregation (MHA) block and locally non-local (LNL) enhancement module.
References
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Book
Markov Random Field Modeling in Image Analysis
TL;DR: This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimisation.
Journal ArticleDOI
Incremental Support Vector Learning for Ordinal Regression
TL;DR: Numerical experiments on the several benchmark and real-world data sets show that the incremental algorithm can converge to the optimal solution in a finite number of steps, and is faster than the existing batch and incremental SVOR algorithms.
Journal ArticleDOI
Rail defects: an overview
TL;DR: An overview of rail defects and their consequences from the earliest days of railways to the present day can be found in this paper, where the authors present an overview of the rail defects in the early days of railway systems.