• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.2
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• pp.113-120
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• 2017

In this paper, we present a night-time vehicle detection method using CNN (Convolutional Neural Network) classification. The camera based night-time vehicle detection plays an important role on various advanced driver assistance systems (ADAS) such as automatic head-lamp control system. The method consists mainly of thresholding, labeling and classification steps. The classification step is implemented by existing CIFAR-10 model CNN. Through the simulations tested on real road video, we show that CNN classification is a good alternative for night-time vehicle detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.601-602
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• 2011

In this paper, we propose head/rear lamp detection algorithm for stopped and wrong way vehicle recognition. It is shown that our algorithm detected vehicles based on the experimental analysis about the color information of vehicle's lamps. The simulation results show the detection rate about stopped and wrong way vehicles is achieved over 94% and 96% in the tunnel HD(High Definition) video image.

• Journal of Microbiology and Biotechnology
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• v.31 no.4
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• pp.610-620
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• 2021

There has been increasing interest in the head and neck squamous cell carcinoma (HNSCC) that is caused by high-risk human papillomavirus (HR-HPV) and has posed a significant challenge to Otolaryngologists. A rapid, sensitive, and reliable method is required for the detection of HR-HPV in clinical specimens to prevent and treat HPV-induced diseases. In this study, a multiple cross-linking spiral amplification (MCLSA) assay was developed for the visual detection of HPV-16. In the MCLSA assay, samples were incubated under optimized conditions at 62℃ for 45 min, and after mixing with the SYBR Green I (SGI) dye, the positive amplicons showed bright green fluorescence while the negative amplicons exhibited no obvious change. The specificity test revealed that the developed MCLSA technique had high specificity and could effectively distinguish all five HPV-16 strains from other pathogenic microorganisms. In terms of analytical sensitivity, the limit of detection (LoD) of MCLSA assay was approximately 5.4 × 101 copies/tube, which was 10-fold more sensitive than loop-mediated isothermal amplification (LAMP) and RT-PCR. The detection results of laryngeal cancer specimens collected from 46 patients with suspected HPV infection in the Liaoning region demonstrated that the positive detection rates of MCLSA and hybridized capture 2 kit were 32.61% (15/46). The true positive rate of the MCLSA assay was higher than that of RT-PCR (100% vs. 93.33%) and LAMP (100% vs. 86.67%). Therefore, the MCLSA assay developed in the present study could be a potentially useful tool for the point-of-care (PoC) diagnosis of HR-HPV, especially in resource-limited countries.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.5
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• pp.311-317
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• 2011

The blind spot is an area where drivers visibility does not reach. When drivers change a lane to adjacent lane, they need to give an attention because of the blind spot. If drivers try to change lane without notice of vehicle approaching in the blind spot, it causes a reason to have a car accident. This paper proposes a night-time blind spot vehicle detection using cameras. At nighttime, head-lights are used as characteristics to detect vehicles. Candidates of headlight are selected by high luminance feature and then shape filter and kalman filter are employed to remove other noisy blobs having similar luminance to head-lights. In addition, vehicle position is estimated from detected head-light, using virtual center line represented by approximated the first order linear equation. Experiments show that proposed method has relatively high detection porformance in clear weather independent to the road types, but has not sufficient performance in rainy weather because of various ground reflectors.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.3
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• pp.27-35
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• 2002

A knowledge-based image retrieval technique is image searching methods using some features from the queried image. The materials in this study are automobile head lamps. The input data is composed of characters and images which have various pattern. The numbers, special symbols, and general letters are under the category of the character. The image informations are made up of the distribution of pixel data, statistical analysis, and state of pattern which are useful for the knowledge data. In this paper, we implemented a retrieval system for the scientific crime detection at traffic accident using the proposed multi knowledge-based image retrieval technique. The values for the multi knowledge-based image features were extracted from color and gray scale each. With this 22 features, we improved the retrieval efficiency about the color information and pattern information. Visual basic, crystal report and MS access DB were used for this application. We anticipate the efficient scientific detection for the traffic accident and the tracking of suspicious vehicle.

• Pediatric Infection and Vaccine
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• v.22 no.2
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• pp.106-112
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• 2015

Purpose: The long-term administration of antibiotics interferes with bacterial culture in the middle ear fluids (MEFs) of young children with otitis media with effusion (OME). The purpose of this study is to determine whether molecular diagnostics can be used for rapid and direct detection of the bacterial pathogen in culture-negative MEFs. Methods: The specificity and sensitivity of both polymerase chain reaction (PCR) and loop-mediated isothermal amplification (LAMP) to the lytA gene of Streptococcus pneumoniae were comparatively tested and then applied for pneumococcal detection in the clinical MEFs. Results: The detection limit of the PCR assay was approximately $10^4$ colony forming units (CFU), whereas that of LAMP was less than 10 CFU for the detection of S. pneumoniae. Both PCR and LAMP did not amplify nucleic acid at over $10^6$ CFU of H. influenzae or M. catarrhalis, both of which were irrelevant bacterial species. Of 22 culture-negative MEFs from children with OME, LAMP positivity was found in twelve MEFs (54.5%, 12/22), only three of which were PCR-positive (25%, 3/12). Our results showed that the ability of LAMP to detect pneumococcal DNA is over four times higher than that of PCR (P<0.01). Conclusions: As a high-resolution tool able to detect nucleic acid levels equivalent to <10 CFU of S. pneumoniae in MEFs without any cross-reaction with other pathogens, lytA -specific LAMP may be applied for diagnosing pneumococcus infection in OME as well as evaluating the impact of a pneumococcal conjugate vaccine against OME.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.1
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• pp.8-15
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• 2011

This paper presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting vehicle headlights and taillights using techniques of image segmentation and clustering. First, in order to effectively extract spotlight of interest, a pre-signal-processing process based on camera lens filter and labeling method is applied on road-scene images. Second, to spatial clustering vehicle of detecting lamps, a grouping process use light tracking method and locating vehicle lighting patterns. For simulation, we are implemented through Da-vinci 7437 DSP board with visible light mono-camera and tested it in urban and rural roads. Through the test, classification performances are above 89% of precision rate and 94% of recall rate evaluated on real-time environment.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.5
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• pp.325-333
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• 2015

Vision based night-time vehicle detection has been an emerging research field in various advanced driver assistance systems(ADAS) and automotive vehicle as well as automatic head-lamp control. In this paper, we propose night-time vehicle detection method based on multi-class support vector machine(SVM) that consists of thresholding, labeling, feature extraction, and multi-class SVM. Vehicle light candidate blobs are extracted by local mean based thresholding following by labeling process. Seven geometric and stochastic features are extracted from each candidate through the feature extraction step. Each candidate blob is classified into vehicle light or not by multi-class SVM. Four different multi-class SVM including one-against-all(OAA), one-against-one(OAO), top-down tree structured and bottom-up tree structured SVM classifiers are implemented and evaluated in terms of vehicle detection performances. Through the simulations tested on road video sequences, we prove that top-down tree structured and bottom-up tree structured SVM have relatively better performances than the others.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.10
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• pp.1443-1449
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• 2013

Automatic detection of vehicles in front is an integral component of many advanced driver-assistance system, such as collision mitigation, automatic cruise control, and automatic head-lamp dimming. Regardless day and night, tail-lights play an important role in vehicle detecting and status recognizing of driving in front. However, some drivers do not know the status of the tail-lights of vehicles. Thus, it is required for drivers to inform status of tail-lights automatically. In this paper, a recognition method of status of tail-lights based on video processing and recognition technology is proposed. Background estimation, optical flow and Euclidean distance is used to detect vehicles entering tollgate. Then saliency map is used to detect tail-lights and recognize their status in the Lab color coordinates. As results of experiments of using tollgate videos, it is shown that the proposed method can be used to inform status of tail-lights.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.5
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• pp.297-305
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• 2015

AFLS (Adaptive front lighting System) is being applied to improve safety in driving automotive at night. Safe embedded system for controlling head-lamp has to be tightly designed by considering safety requirement of hardware-dependent software, which is embedded in automotive ECU(Electronic Control Unit) hardware under severe environmental noise. In this paper, we propose an adaptive headlight controller with newly-designed symmetric angle sensor compensator, which is integrated with ECU-based adaptive front light system. The proposed system, on which additional backup hardware and emergency control algorithm are integrated, effectively detects abnormal situation and restore safe status of controlling the light-angle in AFLS operations by comparing result in symmetric angle sensor. The controlled angle value is traced into internal memory in runtime and will be continuously compared with the pre-defined lookup table (LUT) with symmetric angle value, which is used in normal operation. The watch-dog concept, which is based on using angle sensor and control-value tracer, enables quick response to restore safe light-controlling state by performing the backup sequence in emergency situation.