Journal ArticleDOI
Automatic Defect Detection on Hot-Rolled Flat Steel Products
TLDR
Test results reveal that three-level Haar feature set is more promising to address the problem of automatic defect detection on hot-rolled steel surface than the other wavelet feature sets as well as texture-based segmentation or thresholding technique of defect detection.Abstract:
Automatic defect detection on hot-rolled steel surface is challenging owing to its localization on a large surface, variation in appearance, and their rare occurrences. It is difficult to detect these defects either by physics-based models or by small-sample statistics using a single threshold. As a result, this problem is focused to derive a set of good-quality defect descriptors from the surface images. These descriptors should discriminate the various surface defects when fed to suitable machine learning algorithms. This research work has evaluated the performance of a number of different wavelet feature sets, namely, Haar, Daubechies 2 (DB2), Daubechies 4 (DB4), biorthogonal spline, and multiwavelet in different decomposition levels derived from 32 × 32 contiguous (nonoverlapping) pixel blocks of steel surface images. We have developed an automated visual inspection system for an integrated steel plant to capture surface images in real time. It localizes defects employing kernel classifiers, such as support vector machine and recently proposed vector-valued regularized kernel function approximation. Test results on 1000 defect-free and 432 defective images comprising of 24 types of defect classes reveal that three-level Haar feature set is more promising to address this problem than the other wavelet feature sets as well as texture-based segmentation or thresholding technique of defect detection.read more
Citations
More filters
Journal ArticleDOI
An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features
TL;DR: This paper proposed a novel defect detection system based on deep learning and focused on a practical industrial application: steel plate defect inspection and employs a baseline convolution neural network to generate feature maps at each stage, and the proposed multilevel feature fusion network (MFN) combines multiple hierarchical features into one feature, which can include more location details of defects.
Journal ArticleDOI
A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects
Kechen Song,Yunhui Yan +1 more
TL;DR: Experimental results demonstrate that the proposed approach presents the performance of defect recognition under the influence of the feature variations of the intra-class changes, the illumination and grayscale changes, and even in the toughest situation with additive Gaussian noise, the AECLBP can still achieve the moderate recognition accuracy.
Journal ArticleDOI
Review of vision-based steel surface inspection systems
TL;DR: This paper attempts to make the first formal review of state-of-art of vision-based defect detection and classification of steel surfaces as they are produced from steel mills using vision- based techniques.
Journal ArticleDOI
Automated Visual Defect Detection for Flat Steel Surface: A Survey
TL;DR: This article attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs and hot- and cold-rolled steel strips.
Journal ArticleDOI
An improved Otsu method using the weighted object variance for defect detection
TL;DR: An improved Otsu method, named the weighted object variance (WOV), is proposed in this research to detect defects on product surfaces and provides better segmentation results.
References
More filters
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
A wavelet tour of signal processing
TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Book
An Introduction to Support Vector Machines
TL;DR: This book is the first comprehensive introduction to Support Vector Machines, a new generation learning system based on recent advances in statistical learning theory, and introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods.
Journal ArticleDOI
Computer-Vision-Based Fabric Defect Detection: A Survey
TL;DR: This paper attempts to present the first survey on fabric defect detection techniques presented in about 160 references, and suggests that the combination of statistical, spectral and model-based approaches can give better results than any single approach.
Journal ArticleDOI
Design of prefilters for discrete multiwavelet transforms
TL;DR: The authors propose a general algorithm to compute multi wavelet transform coefficients by adding proper premultirate filter banks before the vector filter banks that generate multiwavelets, which indicates that the energy compaction for discrete multiwavelet transforms may be better than the one for conventional discrete wavelet transforms.