A local annular contrast based real-time inspection algorithm for steel bar surface defects

Li, Wu-bin; Lu, Changhou; Zhang, Jianchuan

doi:10.1016/j.apsusc.2012.03.007

Cited by 50 publications

(36 citation statements)

References 4 publications

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“…The first category includes anomaly detection methods that do not require any prior model of object's structure. Different types of filters [3,6], such as median filter or Gaussian filter, morphological operations [13], and histogram equalization [14], have been all applied for noise reduction, image enhancement, with the aim to improve the contrast between the anomaly and the non-anomalous background. These tools, followed by pattern recognition methods [15,16] or thresholding operations [7,8], illustrate the core architecture of this type of methods.…”

Section: State Of the Artmentioning

confidence: 99%

“…These tools, followed by pattern recognition methods [15,16] or thresholding operations [7,8], illustrate the core architecture of this type of methods. First-order gradient filter followed by thresholding is one of the most commonly used approach in this category [6,8,17]. More recently, state-of-the-art image processing methods, such as multi-resolution representation [18], sparse dictionary learning [19] and variational methods [20], have all been applied for automatic anomaly detection.…”

Section: State Of the Artmentioning

confidence: 99%

“…During the past decades, automated visual inspection (AVI) systems have been broadly used for the inspection of a wide range of "objects" such as fabrics [1,2,3], nuclear fuel rods [4], steel [5,6,7,8] or even food [9] to cite few examples. The main role of a computer-vision system is to provide detailed description of the inspected product, from one or several images, in order to detect and classify the possible occurrence of any type of anomaly.…”

Section: Introductionmentioning

confidence: 99%

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Statistical decision methods in the presence of linear nuisance parameters and despite imaging system heteroscedastic noise: Application to wheel surface inspection

2018

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To cite this version:Karim Tout, Rémi Cogranne, Florent Retraint. Statistical decision methods in the presence of linear nuisance parameters and despite imaging system heteroscedastic noise: Application to wheel surface inspection. Signal Processing, Elsevier, 2018, 144, pp. AbstractThis paper proposes a novel method for fully automatic anomaly detection on objects inspected using an imaging system. In order to address the inspection of a wide range of objects and to allow the detection of any anomaly, an original adaptive linear parametric model is proposed; The great flexibility of this adaptive model offers highest accuracy for a wide range of complex surfaces while preserving detection of small defects. In addition, because the proposed original model remains linear it allows the application of the hypothesis testing theory to design a test whose statistical performances are analytically known. Another important novelty of this paper is that it takes into account the specific heteroscedastic noise of imaging systems. Indeed, in such systems, the noise level depends on the pixels' intensity which should be carefully taken into account for providing the proposed test with statistical properties. The proposed detection method is then applied for wheels surface inspection using an imaging system. Due to the nature of the wheels, the different elements are analyzed separately. Numerical results on a large set of real images show both the accuracy of the proposed adaptive model and the sharpness of the ensuing statistical test.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical decision methods in the presence of linear nuisance parameters and despite imaging system heteroscedastic noise: Application to wheel surface inspection

2018

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“…In steel manufacturing conditions, many methods have been developed to improve the quality of steel and achieve automatic inspection. In this introduction, we briefly mention the various inspection techniques that have been developed for steel manufacturing environments, which include ultrasonic detection methods for stainless steel spot welds 2) and ductile iron, 3) quality monitoring using voltage signals for small-scale resistance spot welding, 4) 3D X-ray tomography inspection for fatigue in aluminum die castings, 5) and optical inspection techniques for slabs, 6,7) steel bar, 8,9) billets, 10) and silicon steel. 11) Although the several studies mentioned above have achieved good performance and can effectively detect their respective defects, it is difficult to apply them directly to defect detection in thick plate surfaces because each of these methods is optimized for a…”

Section: Introductionmentioning

confidence: 99%

Detection of Periodic Defects Using Dual-Light Switching Lighting Method on the Surface of Thick Plates

et al. 2015

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Currently, automated inspection algorithms are widely used to ensure high-quality products and achieve high productivity in the steel making industry. In this paper, we propose a vision-based method to detect periodic defects in the surface of thick plates. To minimize the influence of a non-uniform surface property and improve the accuracy of the detection rate, a detection method based on dual-light switching lighting (DLSL) proposed. In general, single lighting (SL) methods cannot well represent the steel surface because the surface features are not uniform and strongly vary according to lighting conditions. In the DLSL method, defective regions are represented by a black and white pattern, regardless of shape, size, or orientation. Therefore, defects can be found by the black and white patterns in the corresponding images. Gabor filtering was used to find defective regions and reduce the false positive rates. To find the periodic candidates of defects, we process the period searching using manufacturing information. To identify periodic defects from among the defect candidates, we use "similarity of shapes" features with a support vector machine (SVM) classifier. The experimental results show that the proposed algorithm is effective at detecting periodic defects on the surface of thick plates.

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“…Then, ROD detection algorithm based on local annular contrast is proposed in reference. 9) This algorithm is of high speed and accuracy for ROD, but it can merely detect steel bar defects, such as pit, overfill and scratches. Ref.…”

Section: Introductionmentioning

confidence: 99%

A Fast Detection Method for Region of Defect on Strip Steel Surface

et al. 2015

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In order to meet the increasing demands of high efficiency and accuracy for strip steel production line, a fast detection method for region of defect (ROD) on strip steel surface is proposed in this paper. Firstly, the efficiency requirement of ROD detection algorithm is described. Secondly, mean filter improved in speed is used to filter noise. Then, five statistical projection features are extracted from detection region on surface image. Finally, based on distinct feature vector dataset, extreme learning machine (ELM) classifier, region of background (ROB) pre-detection and classifiers selection are combined together to realize two-class classification of ROD and ROB. Experimental results show that the novel method proposed in this paper not only is of high detection accuracy and efficiency but also can satisfy on-line ROD detection.

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A local annular contrast based real-time inspection algorithm for steel bar surface defects

Cited by 50 publications

References 4 publications

Statistical decision methods in the presence of linear nuisance parameters and despite imaging system heteroscedastic noise: Application to wheel surface inspection

Statistical decision methods in the presence of linear nuisance parameters and despite imaging system heteroscedastic noise: Application to wheel surface inspection

Detection of Periodic Defects Using Dual-Light Switching Lighting Method on the Surface of Thick Plates

A Fast Detection Method for Region of Defect on Strip Steel Surface

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