• Journal of the Society of Disaster Information
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• v.16 no.1
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• pp.87-95
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• 2020

Purpose: In this paper, we propose an algorithm for detecting respiratory rate and heart beat of a driver of car by exploiting Doppler radar, and verifying the feasibility of the study through experiments. Method: In this paper, we propose a weighted peak detection technique using peak frequency values. The tests are performed in stop-state and driving-state, and the experiment result is analyzed by two proposed algorithms. Result: The results showed more than 95% and 96% accuracy of respiratory and heart rate, respectively. It also showed more than 72% and 84% accuracy of those even for driving experiments. Conclusion: The proposed detection scheme for vital signs can be used for the safety of the driver as well as for prevention of a large size of car accidents.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.100-108
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• 2016

In this paper, we propose a method to achieve improved number plate detection for mobile devices by applying a multiple convolutional neural network (CNN) approach. First, we processed supervised CNN-verified car detection and then we applied the detected car regions to the next supervised CNN-verifier for number plate detection. In the final step, the detected number plate regions were verified through optical character recognition by another CNN-verifier. Since mobile devices are limited in computation power, we are proposing a fast method to recognize number plates. We expect for it to be used in the field of intelligent transportation systems.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.2
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• pp.121-128
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• 2008

Accurate voice activity detection have a great impact on performance of speech applications including speech recognition, speech coding, and speech communication. In this paper, we propose methods for voice activity detection that can adapt to various car noise situations during driving. Existing voice activity detection used various method such as time energy, frequency energy, zero crossing rate, and spectral entropy that have a weak point of rapid. decline performance in noisy environments. In this paper, the approach is based on existing spectral entropy for VAD that we propose voice activity detection method using MFB(Met-frequency filter banks) spectral entropy, gradient FFT(Fast Fourier Transform) spectral entropy. and gradient MFB spectral entropy. FFT multiplied by Mel-scale is MFB and Mel-scale is non linear scale when human sound perception reflects characteristic of speech. Proposed MFB spectral entropy method clearly improve the ability to discriminate between speech and non-speech for various in noisy car environments that achieves 93.21% accuracy as a result of experiments. Compared to the spectral entropy method, the proposed voice activity detection gives an average improvement in the correct detection rate of more than 3.2%.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.147-150
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• 2001

This paver has presented the architecture and function of the traffic signal enforcement system to detect and capture a image of the violating car in the street intersection. Also in the paper, the algorithm and method of detecting the violation car have been presented and the microwave detection method has been explained. And then this paper has showed the operation software interface for system and presented the experiment data carried out in the field.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.228-239
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• 2022

In this paper, we propose an unmanned vehicle scratch detection deep learning model for car sharing services. Conventional scratch detection models consist of two steps: 1) a deep learning module for scratch detection of images before and after rental, 2) a manual matching process for finding newly generated scratches. In order to build a fully automatic scratch detection model, we propose a one-step unmanned scratch detection deep learning model. The proposed model is implemented by applying transfer learning and fine-tuning to the deep learning model that detects changes in satellite images. In the proposed car sharing service, specular reflection greatly affects the scratch detection performance since the brightness of the gloss-treated automobile surface is anisotropic and a non-expert user takes a picture with a general camera. In order to reduce detection errors caused by specular reflected light, we propose a preprocessing process for removing specular reflection components. For data taken by mobile phone cameras, the proposed system can provide high matching performance subjectively and objectively. The scores for change detection metrics such as precision, recall, F1, and kappa are 67.90%, 74.56%, 71.08%, and 70.18%, respectively.

• Journal of Radiation Protection and Research
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• v.35 no.4
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• pp.167-171
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• 2010

Fixed radiation portal monitors (RPMs) deployed at border, seaport, airport and key traffic checkpoints have played an important role in preventing the illicit trafficking and transport of nuclear and radioactive materials. However, the RPM is usually large and heavy and can't easily be moved to different locations. These reasons motivate us to develop a mobile radiation detection system. The objective of this paper is to report our experience on developing the mobile radiation detection system for search and detection of nuclear and radioactive materials during road transport. Field tests to characterize the developed detection system were performed at various speeds and distances between the radioactive isotope (RI) transporting car and the measurement car. Results of measurements and detection limits of our system are described in this paper. The mobile radiation detection system developed should contribute to defending public's health and safety and the environment against nuclear and radiological terrorism by detecting nuclear or radioactive material hidden illegally in a vehicle.

• Journal of Sensor Science and Technology
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• v.27 no.2
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• pp.137-141
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• 2018

Detecting the ego-lane of a vehicle (the lane on which the vehicle is currently running) is one of the basic techniques for a smart car. Vision sensing is a widely-used method for the ego-lane detection. Existing studies usually find road lane lines by detecting edge pixels in the image from a vehicle camera, and then connecting the edge pixels using Hough Transform. However, this approach takes rather long processing time, and too many straight lines are often detected resulting in false detections in various road conditions. In this paper, we find the lane lines by scanning only a limited number of horizontal lines within a small image region of interest. The horizontal image line scan replaces the edge detection process of existing methods. Automatic thresholding and spatiotemporal filtering procedures are also proposed in order to make our method reliable. In the experiments using real road images of different conditions, the proposed method resulted in high success rate.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.879-881
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• 2014

A method is proposed to achieve improved number plate detection for mobile devices by applying a two-step convolutional neural network (CNN) approach. Supervised CNN-verified car detection is processed first. In the second step, we apply the detected car regions to the second CNN-verifier for number plate detection. Since mobile devices are limited in computing power, we propose a fast method to detect number plates. We expect to use in the field of intelligent transportation systems (ITS).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2677-2685
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• 2013

Car running data collected from the vehicle is a native image data and sensing data of it. Hence, it can be used as a set of objective data based on which events that took place outside the car can be analyzed and determined. In this paper, we designed and implemented a emergency situation detection system to sense, store, and analyze signals related to car movements, driver's various operation states, collision pulse, etc when a car collision accident occurs on the actual road by sensing and analyzing the car movements and driver's operation status. The suggested system provides information on the driver's reaction right before the collision, operation state of the vehicle, and physical movement. The collected and analyzed data on vehicle running can be utilized to clarify the cause of a collision accident and to handle it in a just manner. Besides, it can contribute to grasping and correcting wrong driving habits of the driver and to saving.

• Journal of KIISE:Software and Applications
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• v.36 no.2
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• pp.122-130
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• 2009

Conventional approaches for car number plate detection have dealt with those input images having similar sizes and simple background acquired under well organized environment. Thus their performance get reduced when input images include number plates with different sizes and when they are acquired under different lighting conditions. To solve these problem, this paper proposes a new scheme that uses the geometrical features of number plates and their topological information with reference to other features of the car. In the first step, those edges constructing a rectangle are detected and several pixels neighboring those edges are selected as the seed pixels for region growing. For region growing, color and intensity are used as the features, and the result regions are merged to construct the candidate for a number plate if their features are within a certain boundary. Once the candidates for the number plates are generated then their topological relations with other parts of the car such as lights are tested to finally determine the number plate region. The experimental results have shown that the proposed method can be used even for detecting small size number plates where characters are not visible.