Steel Surface Defect Classification Research Articles

Multi-class classification method for strip steel surface defects based on support vector machine with adjustable hyper-sphere

Focusing on strip steel surface defects classification, a novel support vector machine with adjustable hyper-sphere (AHSVM) is formulated. Meanwhile, a new multi-class classification method is proposed. Originated from support vector data description, AHSVM adopts hyper-sphere to solve classification problem. AHSVM can obey two principles: the margin maximization and inner-class dispersion minimization. Moreover, the hyper-sphere of AHSVM is adjustable, which makes the final classification hyper-sphere optimal for training dataset. On the other hand, AHSVM is combined with binary tree to solve multi-class classification for steel surface defects. A scheme of samples pruning in mapped feature space is provided, which can reduce the number of training samples under the premise of classification accuracy, resulting in the improvements of classification speed. Finally, some testing experiments are done for eight types of strip steel surface defects. Experimental results show that multi-class AHSVM classifier exhibits satisfactory results in classification accuracy and efficiency.

A New Ensemble Approach based on Deep Convolutional Neural Networks for Steel Surface Defect classification

Steel surface defect recognition is a crucial component of automated steel surface inspection system which influences the quality of steel greatly. To improve the accuracy rate, an ensemble approach that integrating different deep convolutional neural networks (DCNNs) is proposed in this paper. Firstly, three different DCNNs are trained respectively with data augmentation to reduce over-fitting. Various optimization methods and tricks are used to reduce the error in the training procedure. Secondly, three well-trained models are combined. The experimental results show that the proposed approach made a state-of-art performance on accuracy rate and robustness in steel surface defect classification.

Steel Surface Defect Classification Based on Discriminant Manifold Regularized Local Descriptor

Steel surface demonstrates various sorts of defects due to the production technique and environment. The appearance of defect is in much more random pattern than that of the normal texture image. Therefore, it is challenging to capture the discriminant information to categorize the defects. The defect image is out of image registration in grayscale, and thus, the local descriptor is inclined to be utilized for feature extraction. In the previous works, involving a local descriptor for categorizing the defect images, the thresholding operator participates in the hand-crafted feature extraction, such as local binary patterns and histogram of oriented gradient, leading to sub-optimal features. By introducing the learning mechanism into the construction of local descriptor, a novel algorithm named discriminant manifold regularized local descriptor (DMRLD) is proposed to conduct the defect classification task in this paper. First, the DMRLD computes the dense pixel difference vector (DPDV) to draw the local information of defect images. Then, the manifold of these DPDVs can be constructed by searching for a number of linear models to represent the feature. In order to enhance the discriminant ability of the feature, a projection on the manifold is learned for achieving a low-dimensional subspace. Finally, the manifold distance defined in the subspace can accomplish the matching task to get the category of the defect image. The proposed algorithm is first applied on the Kylberg texture dataset to evaluate the texture feature extraction performance, and then the experiments on the real steel surface defect dataset are conducted to illustrate the effectiveness of DMRLD compared with other local descriptors.

Steel surface defect classification using multiple hyper-spheres support vector machine with additional information

A novel multiple hyper-spheres support vector machine with additional information (MHSVM+) is proposed for multi-class steel surface defects classification. Originated from binary twin hyper-spheres support vector machine, MHSVM+ uses hyper-sphere to solve classification decision problem. Differently, MHSVM+ is a multi-class classifier, where it builds a corresponding hyper-sphere for each type of defect dataset. Moreover, MHSVM+ introduces learning paradigm using additional information, which means it can learn additional information hidden in defect dataset. Two types of additional information are provided: local neighbor information and local density information. Local neighbor information contains local classification results for defect samples. And local density information is used to capture label noise, isolated samples and important samples in defect dataset. The above two types of additional information are introduced into MHSVM+ model. Finally, MHSVM+ classifier is used to classify six types of steel surface defects. Experimental results show that the novel multi-class classifier has perfect classification accuracy for defect dataset, especially corrupted defect dataset.

Steel surface defects recognition based on multi-type statistical features and enhanced twin support vector machine

For steel surface defect recognition, feature extraction and classification are very important steps. In this paper, multi-type statistical features and enhanced twin support vector machine classifier are formulated and applied. Firstly, four types of statistical features for different attributes of defect region are proposed. They are insensitive to affine transformation in scale and rotation. And those attributes include shape distance and local binary pattern operators with sign and magnitude. Then, dummy boundary samples and representative samples are extracted from steel surface defect dataset. Dummy boundary samples include the sparse boundary information of dataset. They can reduce the adverse impact of noise samples. Representative samples with local and global properties are used to replace samples with quadratic loss. They can exclude noise samples. Based on dummy boundary samples and representative samples, enhanced twin support vector machine is formulated. On one hand, it can solve multi-class classification problem. On the other hand, it has anti-noise ability and high classification efficiency. At last, enhanced twin support vector machine classifier and multi-type statistical features are applied to recognize five types of steel surface defects. The experimental results show that our proposed multi-class classifier has perfect performance in efficiency and accuracy. And multi-type statistical features are in favor of improving classification performance.

Review of vision-based steel surface inspection systems

Steel is the material of choice for a large number and very diverse industrial applications. Surface qualities along with other properties are the most important quality parameters, particularly for flat-rolled steel products. Traditional manual surface inspection procedures are awfully inadequate to ensure guaranteed quality-free surface. To ensure stringent requirements of customers, automated vision-based steel surface inspection techniques have been found to be very effective and popular during the last two decades. Considering its importance, 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. It is observed that majority of research work has been undertaken for cold steel strip surfaces which is most sensitive to customers' requirements. Work on surface defect detection of hot strips and bars/rods has also shown signs of increase during the last 10 years. The review covers overall aspects of automatic steel surface defect detection and classification systems using vision-based techniques. Attentions have also been drawn to reported success rates along with issues related to real-time operational aspects.

Strip Steel Surface Defect Classification Method Based on Enhanced Twin Support Vector Machine

The strip steel surface defect classification belongs to multi-class classification. It demands high classification accuracy and efficiency. However, traditional methods are not fit for abnormal datasets, such as the large-scale, sparse, unbalanced and corrupted dataset. So a novel classification method is proposed in this paper based on enhanced twin support vector machine (TWSVM) and binary tree. According to the density information, the large-scale dataset is pruned, the sparse dataset is added with unlabeled samples, and TWSVM is improved to multi-density TWSVM (MDTWSVM) which has efficient successive overrelaxation (SOR) algorithm. Finally, MDTWSVM and binary tree are combined together to realize multiclass classification. Some experiments are done on the strip steel surface defect datasets with the proposed algorithm. Experimental results show that MDTWSVM has higher accuracy and efficiency than the other methods of multi-class classification for the strip steel surface defect.

Detection of steel defect using the image processing algorithms

Abstract:In this paper, detection and classification of steel surface defects is investigated. Imageprocessing algorithms are applied for detecting four popular kind of steel defects, i.e.,hole, scratch, Coil break and rust. The results show that the applied algorithms have agood performance on steel defect detection. Numerical results indicate that theimplemented image processing algorithms have 88.4%, 78%, 90.4%, 90.3 % accuracyrespectively on the hole, scratch, Coil break and rust defect.