Steel-surface defect detection using a switching-lighting scheme.

In this paper a novel filtering scheme combined with a lighting method is proposed for defect detection in steel surfaces. A steel surface has non-uniform brightness and various shaped defects, which cause difficulties in defect detection. To solve this problem we propose a sub-optimal filtering that is combined with a switching-lighting method. First, dual-light switching lighting (DLSL) is explained, which decreases the effect of non-uniformity of surface brightness and improves the detection accuracy. By using the DLSL method, defects are represented as alternated black and white patterns regardless of the size, shape, or orientation of defects. Therefore, defects can be detected by finding alternated black and white patterns. Second, we propose a scheme for detecting defects in steel-surface images acquired using the DLSL method. The presence of scales strongly affects the optical properties of the surface. Moreover, the textures of steel-plate images vary greatly because of the temperature and grade of steel. Therefore, conventional filter-design methods are not effective for different image textures. A sub-optimal scheme based on an optimized general-finite impulse-response filter is also proposed. Finally, experimental results conducted on steel-surface images from an actual steel-production line show the effectiveness of the proposed algorithm.

Algorithm for detecting seam cracks in steel plates using a Gabor filter combination method.

Presently, product inspection based on vision systems is an important part of the steel-manufacturing industry. In this work, we focus on the detection of seam cracks in the edge region of steel plates. Seam cracks are generated in the vertical direction, and their width range is 0.2-0.6 mm. Moreover, the gray values of seam cracks are only 20-30 gray levels lower than those of the neighboring surface. Owing to these characteristics, we propose a new algorithm for detecting seam cracks using a Gabor filter combination method. To enhance the performance, we extracted features of seam cracks and employed a support vector machine classifier. The experimental results show that the proposed algorithm is suitable for detecting seam cracks.

Defect detection for corner cracks in steel billets using a wavelet reconstruction method.

Presently, automatic inspection algorithms are widely used to ensure high-quality products and achieve high productivity in the steelmaking industry. In this paper, we propose a vision-based method for detecting corner cracks on the surface of steel billets. Because of the presence of scales composed of oxidized substances, the billet surfaces are not uniform and vary considerably with the lighting conditions. To minimize the influence of scales and improve the accuracy of detection, a detection method based on a visual inspection algorithm is proposed. Wavelet reconstruction is used to reduce the effect of scales. Texture and morphological features are used to identify the corner cracks among the defective candidates. Finally, the experimental results show that the proposed algorithm is effective in detecting corner cracks on the surfaces of the steel billets.

Real-time defect detection of steel wire rods using wavelet filters optimized by univariate dynamic encoding algorithm for searches.

We propose a new defect detection algorithm for scale-covered steel wire rods. The algorithm incorporates an adaptive wavelet filter that is designed on the basis of lattice parameterization of orthogonal wavelet bases. This approach offers the opportunity to design orthogonal wavelet filters via optimization methods. To improve the performance and the flexibility of wavelet design, we propose the use of the undecimated discrete wavelet transform, and separate design of column and row wavelet filters but with a common cost function. The coefficients of the wavelet filters are optimized by the so-called univariate dynamic encoding algorithm for searches (uDEAS), which searches the minimum value of a cost function designed to maximize the energy difference between defects and background noise. Moreover, for improved detection accuracy, we propose an enhanced double-threshold method. Experimental results for steel wire rod surface images obtained from actual steel production lines show that the proposed algorithm is effective.

Pinhole detection in steel slab images using Gabor filter and morphological features.

Presently, product inspection for quality control is becoming an important part in the steel manufacturing industry. In this paper, we propose a vision-based method for detection of pinholes in the surface of scarfed slabs. The pinhole is a very tiny defect that is 1-5 mm in diameter. Because the brightness in the surface of a scarfed slab is not uniform and the size of a pinhole is small, it is difficult to detect pinholes. To overcome the above-mentioned difficulties, we propose a new defect detection algorithm using a Gabor filter and morphological features. The Gabor filter was used to extract defective candidates. The morphological features are used to identify the pinholes among the defective candidates. Finally, the experimental results show that the proposed algorithm is effective to detect pinholes in the surface of the scarfed slab.