• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1026-1031
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• 2021

With the development of global automotive technology and the expansion of market size, demand for vehicles is increasing, which is leading to a decrease in the number of passengers on the road and an increase in the number of vehicles on the road. This causes traffic jams, and in order to solve these problems, the number of illegal vehicles continues to increase. Various technologies are being studied to crack down on these illegal activities. Previously developed systems use trigger equipment to recognize vehicles and photograph vehicles using infrared cameras to detect the number of passengers on board. In this paper, we propose a vehicle occupant detection system with deep learning model techniques without exploiting existing system-applied trigger equipment. The proposed technique proposes a system to detect vehicles by establishing triggers within images and to apply deep learning object recognition models to detect real-time boarding personnel.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.116-125
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• 2014

Road conditions can provide important information for driving safety in driving assistance systems. The input images usually include unnecessary information and they need to be analyzed only in a region of interest (ROI) to reduce the amount of computation. In this paper, a vision-based road ROI determination algorithm is proposed to detect the road region using the positional information of a vanishing point and line segments. The line segments are detected using Canny's edge detection and Hough transform. The vanishing point is traced by a Kalman filter to reduce the false detection due to noises. The road ROI can be determined automatically and adaptively in every frame after initialization. The proposed method is implemented using C++ and the OpenCV library, and the road ROIs are obtained from various video images of black boxes. The results show that the proposed algorithm is robust.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.30-39
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• 2007

This paper is concerned with development of system to detect an obstructive vehicle which is an essential prerequisite for autonomous driving system of ASV(Advanced Safety Vehicle). First, the boundary of driving lanes is detected by a Kalman filter through the front image obtained by a CCD camera. Then, lanes are recognized by regression analysis of the detected boundary. Second, parameters of road curvature within the detected lane are used as input in error-BP algorithm to recognize the driving direction. Finally, an obstructive vehicle that enters into the detection region can be detected through setting detection fields of the front and lateral side. The experimental results showed that the proposed system has high accuracy more than 90% in the recognition rate of driving direction and the detection rate of an obstructive vehicle.

• 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.

• Journal of Korean Society of Forest Science
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• v.95 no.6
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• pp.631-635
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• 2006

Single-strand conformation polymorphism (SSCP) analysis of MD and JWB phytopalsma isolates which amplified PCR products using the R16F2n/R2 phytoplamsa universal primer pair were compared for variations of their nucleotide sequence. The MD and JWB phytoplasmas were clearly distinct each of the band patterns from about 1.2 kb PCR products. To clearly distinct of close SSCP band patterns, the MD and JWB phytoplasma PCR products were mixed and performed to detect their polymorphism. The SSCP band patterns show all of bands of MD and JWB on single lane and easily distinct their each band patterns. The PCR-SSCP analysis was possible to detect of 1.2 kb nucleotide sequence and near close band patterns were easily distinct by mixing two samples.

• Journal of Korean Society of Forest Science
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• v.102 no.2
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• pp.223-228
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• 2013

Phytoplasmas were detected consistently in 42 mulberry cultivars showing dwarf disease using DNA analysis by amplification with phytoplasma universal primer pairs P1/P7 (about 1.8 kb and R16F2n/R2 (about 1.2 kb). The point mutation from 42 cultivars of mulberry tree was detected by single-strand conformation polymorphism (SSCP) analysis. The SSCP profiles were clearly observed from all of cultivars in 8% polyacrylamide gel, electrophoresizing for and running 8-15 hrs. at 150V, $10^{\circ}C$. The MD and JWB phytoplasma PCR products was mixed and electrophoresis was performed to detect their polymorphism. In this results, the SSCP profiles of all bands of MD and JWB were analyzed on single lane and were distinct in their each of band patterns. The SSCP analysis was possible to detect of 1.8 kb and 1.2 kb nucleotide size and near close band patterns were distinct by mix of two samples. Previously, it was only possible to detect of point mutation under 600 bp nucleotide sequence by SSCP analysis but this modification of SSCP technique was possible to detect clearly SSCP band patterns of about 1.8 kb and 1.2 kb nucleotides.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.11
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• pp.1879-1886
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• 2016

In this paper, we propose a real-time recognition algorithm of lanes, stop lines and speed bumps on roads for autonomous vehicles. First, we generate a top-view using the image transmitted from a camera that is installed to see the front of a vehicle. To speed up the processing, we simplify the mapping algorithm in constructing a top-view wherein the region of interest (ROI) is concerned. The features of lanes, stop lines and speed bumps, which are composed of lines, are searched in the edge image of the top-view, then followed by labeling and clustering specialized to detect straight lines. The width of lines, distances from the center of a vehicle, and curvature of each cluster are considered to select final candidates. We verify the proposed algorithm on real roads using the commercial car (KIA K7) which is converted into an autonomous vehicle.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.207-214
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• 2009

This study proposed an advanced warning information system based on real-time traffic conflict analysis. An algorithm to detect and analyze unsafe traffic events associated with car-following and lane-changes using individual vehicle trajectories was developed. A positive guidance procedure was adopted to provide warning information to alert drivers to hazardous traffic conditions derived from the outcomes of the algorithm. In addition, autoregressive integrated moving average (ARIMA) analyses were conducted to investigate the predictability of warning information for the enhancement of information reliability.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1221-1227
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• 2015

Edge detection is one of the important technologies to simplify images in the text, lane and object recognition implementation process, and various studies are actively carried out at home and abroad. Existing edge detection methods include a method to detect edge by applying directional gradient masks in spatial space, and a mathematical morphology-based edge detection method. These existing detection methods show insufficient edge detection results in excessively dark or bright images. In this regard, to complement these drawbacks, we proposed an algorithm using the Sobel and histogram equalization among the existing methods.

• Journal of Microbiology and Biotechnology
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• v.20 no.3
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• pp.569-573
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• 2010

A quantitative detection method for Salmonella in seafood was developed using a SYBR Green-based real-time PCR assay. The assay was developed using pure Salmonella DNA at different dilution levels [i.e., 1,000 to 2 genome equivalents (GE)]. The sensitivity of the real-time assay for Salmonella in seeded seafood samples was determined, and the minimum detection level was 20 CFU/g, whereas a detection level of 2 CFU/ml was obtained for pure culture in water with an efficiency of ${\geq}85%$. The real-time assay was evaluated in repeated experiments with seeded seafood samples and the regression coefficient ($R^2$) values were calculated. The performance of the real-time assay was further assessed with naturally contaminated seafood samples, where 4 out of 9 seafood samples tested positive for Salmonella and harbored cells <100 GE/g, which were not detected by direct plating on Salmonella Chromagar media. Thus, the method developed here will be useful for the rapid quantification of Salmonella in seafood, as the assay can be completed within 2-3 h. In addition, with the ability to detect a low number of Salmonella cells in seafood, this proposed method can be used to generate quantitative data on Salmonella in seafood, facilitating the implementation of control measures for Salmonella contamination in seafood at harvest and post-harvest levels.