Strip Steel Defect Detection Based on Saliency Map Construction Using Gaussian Pyramid Decomposition

Guan, Shengqi

doi:10.2355/isijinternational.isijint-2015-041

Cited by 23 publications

(18 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method constructs a saliency map that highlights the defect regions standing out from the rest of the image, which provide the good foundation for segmentation. Guan et al [10] proposed saliency map construction method using Gaussian pyramid decomposition. Then segmentation is conducted with the saliency map.…”

Section: Introductionmentioning

confidence: 99%

Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet

Zhou

Liu

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

Surface defect segmentation supports real-time surface defect detection system of steel sheet by reducing redundant information and highlighting the critical defect regions for high-level image understanding. Existing defect segmentation methods usually lack adaptiveness to different shape, size and scale of the defect object. Based on the observation that the defective area can be regarded as the salient part of image, a saliency detection model using double low-rank and sparse decomposition (DLRSD) is proposed for surface defect segmentation. The proposed method adopts a low-rank assumption which characterizes the defective sub-regions and defect-free background sub-regions respectively. In addition, DLRSD model uses sparse constrains for background sub-regions so as to improve the robustness to noise and uneven illumination simultaneously. Then the Laplacian regularization among spatially adjacent sub-regions is incorporated into the DLRSD model in order to uniformly highlight the defect object. Our proposed DLRSD-based segmentation method consists of three steps: firstly, using DLRSD model to obtain the defect foreground image; then, enhancing the foreground image to establish the good foundation for segmentation; finally, the Otsu’s method is used to choose an optimal threshold automatically for segmentation. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both subjective and objective tests. Meanwhile, the proposed method is applicable to industrial detection with limited computational resources.

show abstract

Section: Introductionmentioning

confidence: 99%

Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet

Zhou

Liu

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Beyond that, it can provide a product quality analysis report which can be adopted for further improve the overall product quality level of steel strip . So far, the effect and accuracy of current visual surface inspection system is not good enough, strip steel inspection in most of the iron and steel mills is still performed manually by human inspectors, a process which suffers from both low efficiency and high labor intensity and cannot meet the requirement of real‐time online detection . For quality control of steel strip production, to efficiently perform visual surface recognition, the majority of approaches follow the intuitive process of splitting the task into two: steel strip surface defects detection and surface defects classification.…”

Section: Introductionmentioning

confidence: 99%

“…For defect region detection, there is no relatively general algorithm to tackle all kinds of defects detection in industry, it is necessary to find out one as generic as possible. For instance, hole, scratch, coil break, rust defect can be detected by leveraging image processing method, but with low accuracy; Xu et al propose a surface defects detection technique for steel strips through three‐dimensional recovery of their gray‐level images which is very time‐consuming; Guan uses the Gaussian pyramid decomposition to low the resolution and center‐surround difference operation to detect the defect and that will loss information of the images . For defect classification, the most common method is to extract defect features with varied methods then use classifiers to classify defects.…”

Section: Introductionmentioning

confidence: 99%

An End‐to‐End Steel Strip Surface Defects Recognition System Based on Convolutional Neural Networks

Jiang

2016

steel research int.

122

View full text Add to dashboard Cite

Steel strip surface defects recognition is very important to steel strip production and quality control, which needs further improvement. In this paper, an end‐to‐end surface defects recognition system is proposed for steel strip surface inspection. This system is based on the symmetric surround saliency map for surface defects detection and deep convolutional neural networks (CNNs) which directly use the defect image as input and defect category as output for seven classes of steel strip defects classification. The CNNs are trained purely on raw defect images and learned defect features from the training of network, which avoiding the separation between feature extraction and image classification, so that forms an end‐to‐end defects recognition pipeline. To further illustrate the superiority of the defect recognition methods with CNNs, an authoritative and standard steel strip surface defect dataset − NEU is also used to evaluate the defect recognition effect using CNNs. Experimental results demonstrate that the proposed methods perform well in steel strip surface defect detection of different types and achieve a high recognition rate for defect images. In addition, a series of data augmentation methods are discussed to analyze its effect on avoiding over‐fitting for defects recognition.

show abstract

“…The machine vision-based inspection method has been investigated in the past decade. This method has been adopted for many years in facility parts identification and classification, glass products, steel strips, metal surface inspection, and agricultural product identification [13][14][15][16]. Barua used the deviation of a melt-pool temperature gradient from a reference defect-free cooling curve to predict gas porosity in the LMD process [15].…”

mentioning

confidence: 99%

“…Barua used the deviation of a melt-pool temperature gradient from a reference defect-free cooling curve to predict gas porosity in the LMD process [15]. The Gaussian pyramid decomposition was applied to low the resolution and center-surround difference operation for steel strip defects detection, which would lose the image information [14]. Vision-based methods had been used the primitive attributes reflected by local anomalies to detect and segment defects.…”

mentioning

confidence: 99%

Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network

Cui

Zhang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Metal additive manufacturing (AM) is gaining increasing attention from academia and industry due to its unique advantages compared to the traditional manufacturing process. Parts quality inspection is playing a crucial role in the AM industry, which can be adopted for product improvement. However, the traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. This study presented a convolutional neural network (CNN) approach toward robust AM quality inspection, such as good quality, crack, gas porosity, and lack of fusion. To obtain the appropriate model, experiments were performed on a series of architectures. Moreover, data augmentation was adopted to deal with data scarcity. L2 regularization (weight decay) and dropout were applied to avoid overfitting. The impact of each strategy was evaluated. The final CNN model achieved an accuracy of 92.1%, and it took 8.01 milliseconds to recognize one image. The CNN model presented here can help in automatic defect recognition in the AM industry.

show abstract

Strip Steel Defect Detection Based on Saliency Map Construction Using Gaussian Pyramid Decomposition

Cited by 23 publications

References 18 publications

Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet

Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet

An End‐to‐End Steel Strip Surface Defects Recognition System Based on Convolutional Neural Networks

Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network

Contact Info

Product

Resources

About