• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1729-1740
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• 2017

This paper describes a vision-based method that effectively recognize a traffic light. The method consists of two steps of traffic light detection and discrimination. Many related studies have used color information to detect traffic light, but color information is not robust to the varying illumination environment. This paper proposes a new method of traffic light detection based on intensity and saturation. When a traffic light is turned on, the light region usually shows values with high saturation and high intensity. However, when the light region is oversaturated, the region shows values of low saturation and high intensity. So this study proposes a method to be able to detect a traffic light under these conditions. After detecting a traffic light, it estimates the size of the body region including the traffic light and extracts the body region. The body region is compared with five models which represent specific traffic signals, then the region is discriminated as one of the five models or rejected as none of them. Experimental results show the performance of traffic light detection reporting the precision of 97.2%, the recall of 95.8%, and correct recognition rate of 94.3%. These results shows that the proposed method is effective.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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• pp.145-150
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• 2014

This paper presents an effective vision based method for LED traffic light detection at the daytime. First, the proposed method calculates horizontal coordinates to set region of interest (ROI) on input sequence images. Second, the proposed uses color segmentation method to extract region of green and red traffic light. Next, to classify traffic light and another noise, shape filter and haar-like feature value are used. Finally, temporal delay filter with weight is applied to remove blinking effect of LED traffic light, and state and weight of traffic light detection are used to classify types of traffic light. For simulations, the proposed method is implemented through Intel Core CPU with 2.80 GHz and 4 GB RAM, and tested on the urban and rural road video. Average detection rate of traffic light is 94.50 % and average recognition rate of traffic type is 90.24 %. Average computing time of the proposed method is 11 ms.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.2
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• pp.101-108
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• 2015

In this paper, a robust traffic light detection method is proposed by using vision sensor and DGPS(Difference Global Positioning System). The conventional vision-based detection methods are very sensitive to illumination change, for instance, low visibility at night time or highly reflection by bright light. To solve these limitations in visual sensor, DGPS is incorporated to determine the location and shape of traffic lights which are available from traffic light database. Furthermore the geometric relationship between traffic light and vision sensor is used to locate the traffic light in the image by using DGPS information. The empirical results show that the proposed method improves by 51% in detection rate for night time with marginal improvement in daytime environment.

• Journal of Sensor Science and Technology
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• v.14 no.6
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• pp.430-437
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• 2005

In this paper, we proposed a biologically inspired light-adaptive edge detection circuit based on the human retina. A saturating resistive network was suggested for light adaptation and simulated by using HSPICE. The light adaptation mechanism of the edge detection circuit was quantitatively analyzed by using a simple model of the saturating resistive element. A light-adaptive capability of the edge detection circuit was confirmed by using the one-dimensional array of the 128 pixels with various levels of input light intensity. Experimental data of the saturating resistive element was compared with the simulated results. The entire capability of the edge detection circuit, implemented with the saturating resistive network, was investigated through the two-dimensional array of the $64{\times}64$ pixels

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3162-3167
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• 2007

The OPS (Optical Particle Sensor) using light scattering from the particles (real-time measurement without physical contact to the particles) can be used for cleanroom or atmospheric environment monitoring. For particles smaller than 300 nm, the detection efficiency becomes lower as scattered light decreases with particle size. To obtain higher detection efficiency with small particles, the flow field in particle chamber and the detection volume should be designed optimally to achieve maximum scattered light from the particles. In this study, a commercial computational fluid dynamics software FLUENT was used to simulate the gas flow field and particle trajectories with various optical chamber designs for 300 nm PSL particle. For estimation of laser viewing volume, we used a commercial computational optical design program ZEMAX. The results will be a great help in the development of OPS which can measure small particles with higher detection efficiency.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1244-1253
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• 2018

The color of traffic light is sensitive to various illumination conditions. Especially it loses the hue information when oversaturation happens on the lighting area. This paper proposes a traffic light recognition method robust to these illumination variations. The method consists of two steps of traffic light detection and recognition. It just uses the intensity and saturation in the first step of traffic light detection. It delays the use of hue information until it reaches to the second step of recognizing the signal of traffic light. We utilized a deep learning technique in the second step. We designed a deep convolutional neural network(DCNN) which is composed of three convolutional networks and two fully connected networks. 12 video clips were used to evaluate the performance of the proposed method. Experimental results show the performance of traffic light detection reporting the precision of 93.9%, the recall of 91.6%, and the recognition accuracy of 89.4%. Considering that the maximum distance between the camera and traffic lights is 70m, the results shows that the proposed method is effective.

• Journal of Sensor Science and Technology
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• v.20 no.6
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• pp.412-419
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• 2011

In this paper, we used the inter-bit noise detection method in order to reduce the effects of noise light in a visible light identification system that uses a visible LED as a carrier source. A visible light identification system consists of a reader and a transponder. When the enable signal from the reader is detected, the transponder encodes the response data in RZ(Return-to-Zero) bit stream and sends response signal by modulating a visible LED. The reader detects the response signal mixed with noise light, samples the noise voltage in each blank low time between data bits of the RZ signal, and recovers the original data by subtracting the sampled noise from the received signal. In experiments, we improved the signal-to-noise ratio by 20dB using the inter-bit noise detection method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.109-111
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• 2021

Recently, computer vision research using light field cameras has been actively conducted. Since light field cameras have spatial information, various studies are being conducted in fields such as depth map estimation, super resolution, and 3D object detection. In this paper, we propose a method for detecting objects in blur images through a 7×7 array of images acquired through a light field camera. The blur image, which is weak in the existing camera, is detected through the light field camera. The proposed method uses the SSD algorithm to evaluate the performance using blur images acquired from light field cameras.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.437-440
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• 2022

In modern society, the use of the image processing field is increasing rapidly due to the 4th industrial revolution and the development of IoT technology. In particular, edge detection is widely used in various fields as an essential preprocessing process in image processing applications such as image classification and object detection. Conventional methods for detecting an edge include a Sobel edge detection filter, a Roberts edge detection filter, a Prewitt edge detection filter, Laplacian of Gaussian (LoG), and the like. However, existing methods have the disadvantage of showing somewhat insufficient performance of edge detection characteristics in a low-light level environment with low contrast. Therefore, this paper proposes an edge detection algorithm based on contrast analysis to increase edge detection characteristics even in low-light level environments.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.3
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• pp.1181-1184
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• 2015

Background: Accurate measurement of breast mass size is fundamental for treatment planning. We evaluated performance of BreastLight apparatus in detection breast of masses with this in mind. Materials and Methods: From July 2011 to September 2013, a total of 500 women referred to mammography unit in Yazd, Iran for screening were recruited to this study. Performance of BreastLight in detection breast masses regard their sizeing, measured with clinical breast examination (CBE), mammography and sonography, was assessed. Sonographic and mammography examinations were performed according to breast density among women in two groups of women younger (n=105) and older (n=395) than 30 years. Size correlations were performed using Spearman rho analysis. Differences between mass size as assessed with the different methods (mammography, sonography, and clinical examination) and the BreastLight detection were analyzed using $X^2$-trend test. Results: Performance of the BreastLight in detection of lesions smaller than or equal to 1 cm assessed by CBE, mammography and sonography was 4.4%,7.7% and 12.5% and for masses larger than 4 cm was 65%, 100% and 57.1%, respectively. The performance of BreastLight in detection was significantly increased with larger masses (p<0.001). Conclusions: We conclude that clinical measurement of breast cancer size is as accurate as that from mammography or ultrasound. Accuracy can be improved by the use of a simple formula of both clinical and mammographic measurements.