There are several types of steel products, such as wire rods, cold-rolled coils, hot-rolled coils, thick plates, and electrical sheets. Surface stains on cold-rolled coils are considered defects. However, surface stains on thick plates are not considered defects. A conventional optical structure is composed of a camera and lighting module. A defect inspection system that uses a dual lighting structure to distinguish uneven defects and color changes by surface noise is proposed. In addition, an image processing algorithm that can be used to detect defects is presented in this paper. The algorithm consists of a Gabor filter that detects the switching pattern and employs the binarization method to extract the shape of the defect. The optics module and detection algorithm optimized using a simulator were installed at a real plant, and the experimental results conducted on thick steel plate images obtained from the steel production line show the effectiveness of the proposed method.
In this paper, we developed a blowhole detection algorithm using texture analysis. We applied Gabor filter to extract defect candidates and used subsequently texture information to classify defect and pseudo-defect. To increase performance, size filtering and adaptive thresholding method were used. The proposed algorithm was tested on 343 images. The experimental result described in this paper shows that this algorithm was effective and suitable for blowhole detection in steel slabs.
This paper describes application-oriented text localization and character segmentation algorithms in images. The target text in our application includes many unclear characters due to poor environment as well as the fact that their positions are variable in the images. Consequently, it is difficult to expect a high success rate when using existing text localization algorithms that have been developed for generic texts. Therefore, it is necessary to develop a new text localization algorithm. We propose (1) a coarse algorithm for detecting top and bottom boundaries, (2) a fitness function that is used to decide the true text among the text candidates, (3) two kinds of presegmentation algorithms for calculating the fitness function, and (4) a blank-detecting algorithm that determines whether the text is upside down or not. By the proposed algorithms, input upside-down text is rotated automatically without using any supervised or unsupervised learning methods; further, character segmentation can be done in the process of selecting the true text. To evaluate the algorithms, image data captured by the installed recognition system at Pohang Steel Company (POSCO) are used, and experimental results show that the proposed algorithms are fast and reliable.
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.
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.