• Current Optics and Photonics
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• v.1 no.3
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• pp.196-202
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• 2017

Corona discharge is always a sign of failure processes of high-voltage electrical apparatus, including those utilized in electric railway systems. Solar-blind ultraviolet (UV) cameras are effective tools for corona inspection. In this work, we present an automatic railway corona-discharge detection system based on solar-blind ultraviolet detection. The UV camera, mounted on top of a train, inspects the electrical apparatus, including transmission lines and insulators, along the railway during fast cruising of the train. An algorithm based on the Hough transform is proposed for distinguishing the emitting objects (corona discharge) from the noise. The detection system can report the suspected corona discharge in real time during fast cruises. An experiment was carried out during a routine inspection of railway apparatus in Xinjiang Province, China. Several corona-discharge points were found along the railway. The false-alarm rate was controlled to less than one time per hour during this inspection.

• Journal of Periodontal and Implant Science
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• v.47 no.1
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• pp.41-50
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• 2017

Purpose: The aims of the present study were to compare the image quality and visibility of tooth cracks between conventional methods and swept-source optical coherence tomography (SS-OCT) and to develop an automatic detection technique for tooth cracks by SS-OCT imaging. Methods: We evaluated SS-OCT with a near-infrared wavelength centered at 1,310 nm over a spectral bandwidth of 100 nm at a rate of 50 kHz as a new diagnostic tool for the detection of tooth cracks. The reliability of the SS-OCT images was verified by comparing the crack lines with those detected using conventional methods. After performing preprocessing of the obtained SS-OCT images to emphasize cracks, an algorithm was developed and verified to detect tooth cracks automatically. Results: The detection capability of SS-OCT was superior or comparable to that of trans-illumination, which did not discriminate among the cracks according to depth. Other conventional methods for the detection of tooth cracks did not sense initial cracks with a width of less than $100{\mu}m$. However, SS-OCT detected cracks of all sizes, ranging from craze lines to split teeth, and the crack lines were automatically detected in images using the Hough transform. Conclusions: We were able to distinguish structural cracks, craze lines, and split lines in tooth cracks using SS-OCT images, and to automatically detect the position of various cracks in the OCT images. Therefore, the detection capability of SS-OCT images provides a useful diagnostic tool for cracked tooth syndrome.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.511-520
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• 2016

Automatic License Plate Detection (ALPD) is a key technology for a efficient traffic control. It is used to improve work efficiency in many applications such as toll payment systems and parking and traffic management. Until recently, the hand-crafted features made for image processing are used to detect license plates in most studies. It has the advantage in speed. but can degrade the detection rate with respect to various environmental changes. In this paper, we propose a way to utilize a Faster Region based Convolutional Neural Networks (Faster R-CNN) and a Conventional Convolutional Neural Networks (CNN), which improves the computational speed and is robust against changed environments. The module based on Faster R-CNN is used to detect license plate candidate regions from images and is followed by the module based on CNN to remove False Positives from the candidates. As a result, we achieved a detection rate of 99.94% from images captured under various environments. In addition, the average operating speed is 80ms/image. We implemented a fast and robust Real-Time License Plate Detection System.

• Journal of The Korean Society of Integrative Medicine
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• v.5 no.1
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• pp.75-83
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• 2017

Purpose : The parameters used in architectural analysis are muscle thickness, fascicle length, pennation angle, etc. Pennation angle is an important muscle characteristic that plays a significant role in determining a fascicle's force contribution to movement. Ultrasonography has been widely used to obtain the image for measurement of a pennation angle since it is non-invasive and real-time. However, manual assessment in ultrasonographic images is time-consuming and subjective, making it difficult for using in muscle function analysis. Thus, in this study, I proposed an automatic method to extract the pennation angle from the ultrasonographic images of gastrocnemius muscle. Method : The ultrasonographic image obtained from 10 healthy participants's gastrocnemius muscle using for developed automatic measuring program. Automatic measuring program algorithm consists with preprocessing, line detection, line classification, and angle calculation. The resulting image was then used to detect the fascicles and aponeuroses for calculating the pennation angle with the consideration of their distribution in ultrasonographic image. Result : The proposed automatic measurement program showed the stable repeatability of pennation angle calculation. Conclusion : This study demonstrated that the proposed method was able to automatically measure the pennation angle of gastrocnemius, which made it possible to easily and reliably investigate pennation angle more.

• Transportation Technology and Policy
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• v.9 no.4
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• pp.53-58
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• 2012

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.2
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• pp.214-217
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• 2013

In this paper, we proposed an automatic threshold control method for radar warning receiver. Considering the noise level of the environment, this technique can effectively adjust sensitivity level of radar warning receiver and can offer more accurate radar information for aircraft pilot in noisy circumstances.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1367-1370
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• 1987

This paper describes a new automatic bi-level image segmentation algorithm which determines local thresholds by applying a locally adaptive edge detection technique to a variable threshold selection method. Computer simulations show that the performance of the proposed algorithm is more robust than those of automatic global thresholding methods.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1527-1530
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• 2003

Video is an important and challenge media and requires sophisticated indexing schemes for efficient retrieval from visual databases. Scene change detection is the first step for automatic indexing of video data. Recently, several scene change detection algorithms in the pixel and compressed domains have been reported in the literature. However, using pixel methods are computationally complex and are not very robust in detecting scene change detection. In this paper, we propose robust abrupt scene change detection using macroblock type and DC coefficient. Experimental results show that the proposed algorithm is robust for detection of most abrupt scene changes in the compressed domain.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.166-169
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• 2004

Change detection is widely used taxation, military fields, etc. In general, global change detection methods using image difference method, etc, are used in low resolution images and local change detection methods using floating windows, etc, are used in high resolution images. But, these methods have disadvantages in practical use and automatic method for changed area detection should be developed. In this research, characteristics of Kompsat images are reviewed in perspective of change detection and various change detection method applicable to are tested.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.24-31
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• 2023

The solid-state drive (SSD) possesses higher input and output speeds, more resistance to physical shock, and lower latency compared with regular hard disks; hence, it is an increasingly popular storage device. However, tiny components on an internal printed circuit board (PCB) hinder the manual detection of malfunctioning components. With the rapid development of artificial intelligence technologies, automatic detection of components through convolutional neural networks (CNN) can provide a sound solution for this area. This study proposes applying the YOLOv5 model to SSD PCB component detection, which is the first step in detecting defective components. It achieves pioneering state-of-the-art results on the SSD PCB dataset. Contrast experiments are conducted with YOLOX, a neck-and-neck model with YOLOv5; evidently, YOLOv5 obtains an mAP@0.5 of 99.0%, essentially outperforming YOLOX. These experiments prove that the YOLOv5 model is effective for tiny object detection and can be used to study the second step of detecting defective components in the future.