• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.71-73
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• 2021

본 논문에서는 Scene Text Detection의 새로운 프레임워크인 TextFuseNet에 영상처리 관련 기술인 선명화 기법을 제안한다. Scene Text Detection은 야외 간판이나 표지판 등 불특정 배경에서 글자를 인식하는 기술이며, 그중 하나의 프레임워크가 TextFuseNet이다. TextFuseNet은 문자, 단어, 전역 기준으로 텍스트를 감지하는데, 여기서는 영상처리의 기술인 선명화 기법을 적용하여 TextFuseNet의 성능을 향상시키는 것이 목적이다. 선명화 기법은 기존 Sharpening Filter 방법과 Unsharp Masking 방법을 사용하였고 이 중 Sharpening Filter 방법을 적용하였을 때 AP가 0.9% 향상되었음을 확인하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.6
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• pp.1-9
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• 2014

Recently, a study is conducting to image collection and auto detection of attribute information using mobile mapping system. The road sign attribute information detection is difficult because of various size and placement, interference of other facilities like trees. In this study, a text detection method that does not rely on a Korean character template is required to successfully detect the target text when a variety of differently sized texts are present near the target texts. To overcome this, the method of incremental right-to-left blob projection (IRBP) was suggested as a solution; the potential and improvement of the method was also assessed. To assess the performance improvement of the IRBP that was developed, the IRBP method was compared to the existing method that uses Korean templates through the 60 videos of street signs that were used. It was verified that text detection can be improved with the IRBP method.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.1
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• pp.35-45
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• 2013

A autonomous vehicle requires improved and robust perception systems than conventional perception systems of intelligent vehicles. In particular, single sensor based perception systems have been widely studied by using cameras and laser radar sensors which are the most representative sensors for perception by providing object information such as distance information and object features. The distance information of the laser radar sensor is used for road environment perception of road structures, vehicles, and pedestrians. The image information of the camera is used for visual recognition such as lanes, crosswalks, and traffic signs. However, single sensor based perception systems suffer from false positives and true negatives which are caused by sensor limitations and road environments. Accordingly, information fusion systems are essentially required to ensure the robustness and stability of perception systems in harsh environments. This paper describes a perception system for autonomous vehicles, which performs information fusion to recognize road environments. Particularly, vision and laser radar sensors are fused together to detect lanes, crosswalks, and obstacles. The proposed perception system was validated on various roads and environmental conditions with an autonomous vehicle.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.563-565
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• 2010

LED is low power and pro-environment semiconductor element. That can be used not only as the lighting function but also for Visible Light Communication(VLC). The VLC is the communication technology that can send data by blinking a fluorescent or LED using visible spectrum. That velocity of blinking can not be usually recognized by eyesight. Visible Light Communication using LED can be used in many fields. In the field of ITS(Intelligent Transportation System), Under construction on the road, Emitting traffic signs can be applied to transfer the vehicle information. In this paper, Emitting traffic signs in addition to the VLC give information about road condition, safety distance and the lane change. We design Communication protocol to provide safety service and verify protocol by experiment.

• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.81-93
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• 2022

Recently, there have been many studies on object recognition around the vehicle and recognition of traffic signs and traffic lights in autonomous driving. In particular, such the recognition of traffic lights is one of the core technologies in autonomous driving. Therefore, many studies for such the recognition of traffic lights have been performed, the studies based on various deep learning models have increased significantly in recent. In addition, as a high-quality AI training data set for voice, vision, and autonomous driving is released on AIHub, it makes it possible to develop a recognition model for traffic lights suitable for the domestic environment using the data set. In this study, we developed a recognition model for traffic lights that can be used in Korea using the AIHub's training data set. In particular, in order to improve the recognition performance, we used various models of YOLOv4 and YOLOv5, and performed our recognition experiments by defining various classes for the training data. In conclusion, we could see that YOLOv5 shows better performance in the recognition than YOLOv4 and could confirm the reason from the architecture comparison of the two models.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2016.11a
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• pp.249-252
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• 2016

본 연구에서는 연령증가에 따라 운전에 필요한 신체적, 인지적 능력의 변화가 어떻게 나타나는지 설문을 통해 분석하고, 이것이 교통안전 시설물(신호기, 노면표시, 도로표지판)의 필요성 및 적정성을 평가하는 것과 어떤 관계가 있는 지 살펴보고자 하였다. 이를 위해 20세 이상 운전면허 소지자를 대상으로 5개 권역, 500명을 대상으로 설문조사를 실시하여 이를 분석하였다. 운전능력에 대한 자기평가 점수는 종합적 능력, 개별능력(시력, 청력, 색채식별 등) 모두 연령이 증가함에 따라 점수가 낮아지는 것으로 분석되었다. 특히 시력은 연령증가에 따른 감소폭이 크고, 절대적인 평균점수가 다른 항목에 비하여 낮게 나타났다. 시력의 경우 타 신체적, 인지적 능력에 비해 운전 중 기능의 저하에 따른 체감도가 크기 때문인 것으로 판단된다. 설문결과에 따른 분류분석을 통해서는 신체적, 인지적 기능의 저하가 시작되는 연령이 기존의 고령운전자로 판단하는 65세 보다 낮은 48세로 분석되었다. 이것은 연령증가에 따른 기능 저하로 인한 영향을 받는 운전자가 그렇지 않은 운전자보다 더 많다는 것을 의미한다. 또한 시설물에 대한 필요성 인식에 대한 평가 역시 운전능력에서 차이를 나타내는 연령대에서 인식의 차이가 있을 것으로 분석되었다.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.11-21
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• 2011

This study intends to construct scenic byway database and to examine and suggest social experiment of scenic byway. As basis work for the experiment of the scenic byway, we build database by using ITS standard node link management system. The DB includes scenic byway routes of Korea and Japan. The analyses show that the scenic byway in both nations consists of roads, reservation, road sign, vehicle number plate, and it was found that infrastructure and system are inadequate for scenic byway. These experiment can be effectively used for scenic byway in Korea and Japan as the basis data. The results of this experiment will be useful for plan and develop a scenic byway.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3710-3717
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• 1999

For the development of unmanned autonomous vehicle, it is essential to detect obstacles, especially vehicles, in the forward direction of navigation. In order to reliably exclude regions that do not contain obstacles and save a considerable amount of computational effort, it is often necessary to confine computation only to ROI(region of interest)s. A ROI is usually chosen as the interior region of the lane. We propose a computationally simple and efficient method for the detection of lanes based on Hough transform and quadratic curve fitting. The proposed method first employs Hough transform to get approximate locations of lanes, and then applies quadratic curve fitting to the locations computed by Hough transform. We have experimented the proposed method on real outdoor road scene. Experimental results show that our method gives accurate detection of straight and curve lanes, and is computationally very efficient.

• Convergence Security Journal
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• v.21 no.2
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• pp.57-66
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• 2021

Deep neural networks (DNNs) provide excellent performance for image, speech, and pattern recognition. However, DNNs sometimes misrecognize certain adversarial examples. An adversarial example is a sample that adds optimized noise to the original data, which makes the DNN erroneously misclassified, although there is nothing wrong with the human eye. Therefore studies on defense against adversarial example attacks are required. In this paper, we have experimentally analyzed the success rate of detection for adversarial examples by adjusting various parameters. The performance of the ensemble defense method was analyzed using fast gradient sign method, DeepFool method, Carlini & Wanger method, which are adversarial example attack methods. Moreover, we used MNIST as experimental data and Tensorflow as a machine learning library. As an experimental method, we carried out performance analysis based on three adversarial example attack methods, threshold, number of models, and random noise. As a result, when there were 7 models and a threshold of 1, the detection rate for adversarial example is 98.3%, and the accuracy of 99.2% of the original sample is maintained.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.4
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• pp.143-154
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• 2005

This paper proposes the location acquisition methods and LBS(location-based services) system using RFID(radio frequency identification) tags with location information in ubiquitous city. Location information is checked using RFID tags attached to various buildings. stores and road signs. And various LBSs are provided based on the location information. Traditionally, when the location information is obtained by a GPS, the precision of the location information may deteriorate due to geographical displacement of a satellite and GPS receivers, for example shadow zone by building, in-door environment, and heavy cloud. The objective of this paper is to provide a ULID(universal location identification) data structure, a ULID-based location acquisition method and an LBS system, in which precise location information is extracted using RFID tags attached to various buildings, stores and road signs and ULID and also danger of information leakage is minimized so that an LBS of ubiquitous environment can be provided to a user.