• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.91-99
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• 2020

For the shadow removal technology that is the base technology of the image detection system, real-time image processing has a problem that the processing speed is reduced due to the calculation complexity and it is also sensitive to illumination or light because shadows are removed only by the difference in brightness. Therefore, in this paper, we improved real-time performance by reducing the calculation complexity through the removal of the weighting part in order to solve the problem of the conventional system. In addition, we designed and evaluated an image detection system based on a shadow removal algorithm that could improve the shadow recognition rate using a vertical histogram. The evaluation results confirmed that the average speed increased by approximately 5.6ms and the detection rate improved by approximately 5.5%p compared to the conventional image detection system.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.7-17
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• 2001

The purpose of this paper is to develop automatic incident detection algorithm using image based detector in freeway management system. This algorithm was developed by using neutral network for high speed roadway and by using speed and occupancy variable for low speed roadway. The image detector system with the developed automatic incident detection algorithm can detect multi-lane as well as several detect areas for each lane. To evaluate this system, field tests to measure the detecting rate of incidents were performed with other systems which have APID and DES algorithm at high speed roadway(freeway) and low speed roadway(national arterial). As the results of field test, it found that the detect rate of this system was highest rate comparing to other two systems.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.4
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• pp.500-507
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• 2009

This paper presents a novel method which can detect a target in IR image for active protection system. The target in IR image for the active protection system is small, moreover it moves with enormous speed. The proposed algorithm is comprised of robust clutter rejection methods and target optimized detection algorithms for small target, and an advanced method of selecting a final target position in target area, it can work in some milliseconds. The proposed algorithm provides the active protective system with more correct positions than those of radar, so that helps the active protection system can defense all threats with the utmost precision.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.888-896
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• 2018

In this paper, we report detection and recognition of vehicle logo from images captured from street CCTV. Image data includes both the front and rear view of the vehicles. The proposed method is a two-step process which combines image preprocessing and faster region-based convolutional neural network (R-CNN) for logo recognition. Without preprocessing, faster R-CNN accuracy is high only if the image quality is good. The proposed system is focusing on street CCTV camera where image quality is different from a front facing camera. Using perspective transformation the top view images are transformed into front view images. In this system, the detection and accuracy are much higher as compared to the existing algorithm. As a result of the experiment, on day data the detection and recognition rate is improved by 2% and night data, detection rate improved by 14%.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.108-112
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• 2023

Precision manufacturing technology is rapidly developing due to the extreme miniaturization of semiconductor processes to comply with Moore's Law. Accurate and precise alignment, which is one of the key elements of the semiconductor pre-process and post-process, is very important in the semiconductor process. The center detection of wafer align marks plays a key role in improving yield by reducing defects and research on accurate detection methods for this is necessary. Methods for accurate alignment using traditional image sensors can cause problems due to changes in image brightness and noise. To solve this problem, engineers must go directly into the line and perform maintenance work. This paper emphasizes that the development of AI technology can provide innovative solutions in the semiconductor process as high-resolution image and image processing technology also develops. This study proposes a new wafer center detection method through variable thresholding. And this study introduces a method for detecting the center that is less sensitive to the brightness of LEDs by utilizing a high-performance object detection model such as YOLOv8 without relying on existing algorithms. Through this, we aim to enable precise wafer focus detection using artificial intelligence.

• Journal of the Korean Society of Safety
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• v.18 no.4
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• pp.23-27
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• 2003

To improve the road transportation safety, the road traffic data is monitored by applying an image detection system. The road traffic safety is analysed using image processing techniques. For more accurate measurement, the coordinate matching of real road data to image is one of the most essential parts of the image detection technique. The road image is skewed at the input screen, because the video camera is installed at the roadside. A fast and precise algorithm for the coordinate matching is developed to convert image coordinates into road coordinates.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.6
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• pp.345-351
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• 2014

This paper presents pedestrian recognition to improve performance for vehicle safety system or surveillance system. Pedestrian detection method using HOG (Histograms of Oriented Gradients) has showed 90% recognition rate. But if someone takes a picture in the rain, the image may be distorted by rain streaks and recognition rate goes down by 62%. To solve this problem, we applied image decomposition method using MCA (Morphological Component Analysis). In this case, rain removal method improves recognition rate from 62% to 70%. However, it is difficult to apply conventional image decomposition method using MCA on vehicle safety system or surveillance system as conventional method is too slow for real-time system. To alleviate this issue, we propose a rain removal method by using low-pass filter and DCT (Discrete Cosine Transform). The DCT helps separate the image into rain components. The image is removed rain components by Butterworth filtering. Experimental results show that our method achieved 90% of recognition rate. In addition, the proposed method had accelerated processing time to 17.8ms which is acceptable for real-time system.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.171-171
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• 2000

We have developed m innovative surface inspection system for automated quality control for steel products in POSCO. We had ever installed the various kinds of surface inspection systems, such as a linear CCD and a laser typed surface inspection systems at cold rolled strips production lines. But, these systems cannot fulfill the sufficient detection and classification rate, and real time processing performance. In order to increase detection and classification rate, we have used the Dark, Bright and Transition Field illumination and area type CCD camera, and fur the real time image processing, parallel computing has been used. In this paper, we introduced the automatic surface inspection system and real time image processing technique using the Object Detection, Defect Detection, Classification algorithms and its performance obtained at the production line.

• ETRI Journal
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• v.32 no.6
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• pp.881-890
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• 2010

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision-based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand-alone system to detect fire by using video frames acquired through a video acquisition device. A novel fire color model is developed in CIE $L^*a^*b^*$ color space to identify fire pixels. The proposed fire color model is tested with ten diverse video sequences including different types of fire. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. The overall fire detection system's performance is tested over a benchmark fire video database, and its performance is compared with the state-of-the-art fire detection method.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.159-166
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• 2021

COVID-19 has dramatically changed people's daily life. Wearing masks is considered as a simple but effective way to defend the spread of the epidemic. Hence, a real-time and accurate mask wearing detection system is important. In this paper, a deep learning-based mask wearing detection system is developed to help people defend against the terrible epidemic. The system consists of three important functions, which are image detection, video detection and real-time detection. To keep a high detection rate, a deep learning-based method is adopted to detect masks. Unfortunately, according to the suddenness of the epidemic, the mask wearing dataset is scarce, so a mask wearing dataset is collected in this paper. Besides, to reduce the computational cost and runtime, a simple online and real-time tracking method is adopted to achieve video detection and monitoring. Furthermore, a function is implemented to call the camera to real-time achieve mask wearing detection. The sufficient results have shown that the developed system can perform well in the mask wearing detection task. The precision, recall, mAP and F1 can achieve 86.6%, 96.7%, 96.2% and 91.4%, respectively.