• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.237-239
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• 2009

The purpose of this research is to propose a system to detect a strip defect on a iron plate using an image processing, one way of finding defects on an iron plate. An existing way of image processing is using a light source which release a light energy in a certain frequency and a light absorbing display which responds to the light source. This research attempts to detect defects by using the image processing which handles an illumination, without depending on characteristics of light frequency. One of the advantages of this method is that it makes up for the weakness of the existing method which was too difficult for users to notice a defect. Also this method makes it possible to realize a real-time monitoring on a plate of iron.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.247-252
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• 2016

In this paper, we propose a new defect detection technology for micro-defect on the surface of steel products. Due to depth and size of microscopic defect, slop of surface and vibration of strip, the conventional optical method cannot guarantee the detection performance. To solve the above-mentioned problems and increase signal to noise ratio, a novel retro-schlieren method that consists of retro reflector and knife edge is proposed. Moreover dual switching lighting method is also applied to distinguish uneven micro defects and surface noise. In proposed method, defective regions are represented by a black and white pattern. This pattern is detected by a defect detection algorithm with Gabor filter. Experimental results by simulator for sample defects of cold rolled steel show that the proposed method is effective.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.145-153
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• 2009

The purpose of this research is to propose a system to detect a strip defect on a iron plate using an image processing, one way of finding defects on an iron plate. An existing way of image processing is using a light source which release a light energy in a certain frequency and a light absorbing display which responds to the light source. This research attempts to detect defects by using a image processing and multi-Tread which handles an illumination, without depending on characteristics of light frequency. One of the advantages of this method is that it makes up for the weakness of the existing method which was too difficult for users to notice a defect. Also this method makes it possible to realize a real-time monitoring on a plate of iron. The other advantage of this method is that it reduces the price of hardwares on demand to match the frequency of light emitting display and light absorbing display because this method only needs a hardware which is easy to buy in any market.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.19-26
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• 2020

Steel production requires high-quality surfaces with minimal defects. Therefore, the detection algorithms for the surface defects of steel strip should have good generalization performance. To meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. In this paper, we proposed a ShuffleDefectNet defect detection system based on deep learning. The proposed defect detection system exceeds state-of-the-art performance for defect detection on the Northeastern University (NEU) dataset obtaining a mean average accuracy of 99.75%. We train the best performing detection with different amounts of training data and observe the performance of detection. We notice that accuracy and speed improve significantly when use the overall architecture of ShuffleDefectNet.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.3
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• pp.372-377
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• 1998

This paper deals with the detection, feature extraction and classification of surface defects in cold rolled strips. Inspection systems are one of the most important fields in factory automation. Defects such as slipmark and dullmark can be effectively detected with a Gaussian matched filter because their shapes are similar to Gaussian. It is justified that the proposed WF(Wavelet Frame) method could be regarded as multiscale Gaussian matched filter which can be applied to the inspection of cold rolled strip. After a wavelet frame transform, the entropies and moments are computed for each subband which pass through both local low pass filter and nonlinear operator. With these features as input, a MLP(Multi Layer Perceptron) is used as a classifier. The proposed inspection method was applied to the real images with defects, and hence showed good performance. The role of each extracted feature is analyzed by KLT(Karhunen-Loeve Transform).