A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects

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.

https://link.springer.com/content/pdf/10.1186%2F1687-5281-2014-50.pdf

Review of vision-based steel surface inspection systems

Surface defect classification of steels with a new semi-supervised learning method

Defect detection of hot rolled steels with a new object detection framework called classification priority network

Continuous casting is a highly efficient process used to produce most of the world steel production tonnage, but can cause cracks in the semi-finished steel product output. These cracks may cause problems further down the production chain, and detecting them early in the process would avoid unnecessary and costly processing of the defective goods. In order for a crack detection system to be accepted in industry, however, false detection of cracks in non-defective goods must be avoided. This is further complicated by the presence of scales; a brittle, often cracked, top layer originating from the casting process. We present an approach for an automated on-line crack detection system, based on 3D profile data of steel slab surfaces, utilizing morphological image processing and statistical classification by logistic regression. The initial segmentation successfully extracts 80% of the crack length present in the data, while discarding most potential pseudo-defects (non-defect surface features similar to defects). The subsequent statistical classification individually has a crack detection accuracy of over 80% (with respect to total segmented crack length), while discarding all remaining manually identified pseudo-defects. Taking more ambiguous regions into account gives a worst-case false classification of 131 mm within the 30 600 mm long sequence of 150 mm wide regions used as validation data. The combined system successfully identifies over 70% of the manually identified (unambiguous) crack length, while missing only a few crack regions containing short crack segments. The results provide proof-of-concept for a fully automated crack detection system based on the presented method.

/pdf/morphology-based-crack-detection-for-steel-slabs-5boomrkvaj.pdf

Morphology-Based Crack Detection for Steel Slabs

Recently, there has been an increase in the demand for quality control in the steel making industry. This paper proposes a vision-based method for detection of defects in the surfaces of scale-covered steel billets. Scales are formed on the surface of billets owing to the deposition of oxidized substances that are produced during manufacturing. Because of the presence of scales on the billet surface, its characteristics such as brightness and texture in the background region are inconsistent. Moreover, the similarities in the gray-levels of the defect and defect-free regions make it very difficult to accurately detect defects. In order to solve the abovementioned problems and to detect defects more effectively, we propose a new defect detection algorithm, which is based on Gabor filters. The Gabor filters are optimized using a new optimization algorithm known as univariate dynamic encoding algorithm for searches (uDEAS). The algorithm finds the minimum value of the cost function related to the energy separation criteria between the defect and the defect-free regions. Finally, the experimental results conducted on billet surface images from actual steel production line show the effectiveness of the proposed algorithm.

Automatic detection of cracks in raw steel block using Gabor filter optimized by univariate dynamic encoding algorithm for searches (uDEAS)

In the steel-making industry, both the quality and quantity of the products are critical. This work presents a real-time defect detection method for high-speed steel bar in coil (BIC). For good performance characteristics, the detection algorithm must be robust to problems associated with the cylindrical shape of BICs, the presence of noise and nonuniform brightness distribution of images, the various types of defects, and so on. Furthermore, because the target speed is very high, it should have a fast processing time. Therefore, a defect detection algorithm should satisfy the two conflicting requirements of reducing the processing time and improving the efficiency of defect detection. This work proposes an effective real-time defect detection algorithm that can satisfy these conditions. Moreover, to reduce cost, the proposed algorithm is implemented on a PC-based real-time defect detection system without a professional digital signal processing (DSP) board. Experimental results show that the proposed algorithm guarantees both real-time processing and accurate detection.

Real-time vision-based defect inspection for high-speed steel products

Vision-based inspection systems have been widely investigated for the detection and classification of defects in various industrial product. We present a new defect detection algorithm for scale-covered steel billet surfaces. Because of the availability of various kinds of steel, presence of scales, and manufacturing conditions, the features of billet surface images are not uniform. In particular, scales severely change the properties of defect-free surfaces. Moreover, the various kinds of possible defects make their detection difficult. In order to resolve these problems and to improve the detection performance, two methods are proposed. First, undecimated wavelet transform and vertical projection profile are presented. Second, a method for detecting the variations in the block features along the vertical direction is proposed. The former method can effectively detect vertical line defects, and the latter can efficiently detect the remaining defects, except the vertical line defects. The experimental results conducted on billet surface images obtained from actual steel production lines show that the proposed algorithm is effective for defect detection of scale-covered steel billet surfaces.

Vision-based defect detection of scale-covered steel billet surfaces

Periodic torque ripples exist due to non-perfect sinusoidal flux distribution, cogging torque and current measurement errors in permanent magnet synchronous motor (PMSM). These ripples are reflected as periodic oscillations in the motor speed and deteriorate the performance of application of PMSM as a high-precision tracking applications. In this paper, we propose a variable step-size normalized iterative learning control (VSS-NILC) scheme to reduce periodic torque ripples. VSS-NILC is combined to existing PI current controller and generates compensated reference current iteratively from cycle to cycle so as to minimize the mean square torque error. VSS-NILC scheme alter the step-size of the update equation to reduce the conflict between speed of convergence and minimum mean square error (MSE). Consequently VSS-NILC scheme has faster convergence rate and lower mean square torque error. Simulation results show significant improvements in the steady-state torque response and the effectiveness in minimizing torque ripples.

Torque Ripples Minimization in PMSM using Variable Step-Size Normalized Iterative Learning Control

In this paper, we present a new detection algorithm for line defects of scale-covered steel billets. Because of the presence of scales on the billet surface, features of surface images such as brightness and textures are non-uniform. To minimize the influence of scales and to improve the accuracy of detection, a new detection method based on undecimated wavelet transform is proposed. The vertical projection profile of subimage with high-frequency information produced by undecimated wavelet transform is used to detect the line defects. Experimental results conducted on billets surface image from actual steel production line show that the proposed algorithm is capable of detecting line defects on billet surface.

SungHoo Choi

Papers

Automatic detection of cracks in raw steel block using Gabor filter optimized by univariate dynamic encoding algorithm for searches (uDEAS)

Real-time vision-based defect inspection for high-speed steel products

Vision-based defect detection of scale-covered steel billet surfaces

Torque Ripples Minimization in PMSM using Variable Step-Size Normalized Iterative Learning Control

Detection of line defects in steel billets using undecimated wavelet transform