• Title/Summary/Keyword: High-speed Vehicle

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Estimating the Dimension of a Crosswalk in Urban Area - Focusing on Width and Stop Line - (도시부 횡단보도 제원 산정에 관한 연구 - 폭과 정지선을 중심으로 -)

  • Kim, Yoomi;Park, Jejin;Kwon, Sungdae;Ha, Taejun
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.36 no.5
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    • pp.847-856
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    • 2016
  • Recently, with a high level of economic growth, rapid urbanization, population, environment and housing problems were accompanied in Korea. In particular, the traffic problem has become a serious social problem. As the current transportation policy has been carried out, concentrating on traffic flow, in 2015, death rate for pedestrians while walking (1,795 persons) is 38.8% compared to entire death rate in car accident (4,621 persons), so there is need to solve it. Although, crosswalk should make pedestrian cross it safely, it has been made on the basis of the width of road without exact standard for current width of the crosswalk and the location of stop line. Moreover, in the area around many campuses or commercial facilities, crosswalks are set with not considering pedestrian passage, but designed uniformly. Therefore, the purpose of this study is to estimate reasonable dimension of crosswalk considering pedestrian traffic and walking speed and it makes the accident rate lower in the crosswalk, which has a lot of problems including decisions of vehicle traffic signal time, lack of pedestrian's signal timing, pedestrian's crossing of long-distance. The following are the methodology of the study. Firstly, for crosswalk calculation of specifications, examination relating existing regulations and researches dealing with crosswalk, pedestrians and stop line is needed. After analyzing problems of current width of crosswalk and stop line, present the methodology to calculation of specifications and basing on these things, calculation of specifications for crosswalk will be decided. In conclusion, the calculation of specification and improvement of stop line for crosswalk laid out in this study are expected to be utilized as base data in case of establishing relevant safety facilities and standards.

Development of deep learning network based low-quality image enhancement techniques for improving foreign object detection performance (이물 객체 탐지 성능 개선을 위한 딥러닝 네트워크 기반 저품질 영상 개선 기법 개발)

  • Ki-Yeol Eom;Byeong-Seok Min
    • Journal of Internet Computing and Services
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    • v.25 no.1
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    • pp.99-107
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    • 2024
  • Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.