• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.61-64
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• 2024

As research on autonomous driving technology becomes more active, various studies on signal recognition of traffic lights are also being conducted. When recognizing traffic lights with different purposes and shapes, such as pedestrian traffic lights, vehicle-only traffic lights, and right-turn traffic lights, existing classification methods may cause misrecognition problems. Therefore, in this study, we studied a model that allows accurate signal recognition by subdividing the classification of signals according to the purpose and type of traffic lights. A signal recognition model was created by classifying traffic lights according to their shape and purpose into horizontal, vertical, right turn, etc., and by comparing them with the existing signal recognition model based on YOLOv5, it was confirmed that more correct and accurate recognition was possible.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.65-72
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• 2006

The traffic lights detection and recognition system is an essential module of the driver warning and assistance system. A method which is a color vision-based real time detection and recognition of traffic lights is presented in this paper This method has four main modules : traffic signals lights detection module, traffic lights boundary candidate determination module, boundary detection module and recognition module. In traffic signals lights detection module and boundary detection module, the color thresholding and the subtraction value of saturation and intensity in HSI color space and detection probability mask for lights detection are used to segment the image. In traffic lights boundary candidate determination module, the detection mask of traffic lights boundary is proposed. For the recognition module, the AND operator is applied to the results of two detection modules. The input data for this method is the color image sequence taken from a moving vehicle by a color video camera. The recorded image data was transformed by zooming function of the camera. And traffic lights detection and recognition experimental results was presented in this zoomed image sequence.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.92-99
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• 2009

This paper established the range of the wavelength of traffic lights to detection the color of traffic lights and the color component segmentation with the range of the wavelength. Development of traffic light automatic discrimination system is consists of the color detection and the traffic lights recognition. In this thesis, it established the range of the wavelength of traffic lights to detection the color of traffic lights and the color segmentation with the range of the wavelength. By the segmentation, the traffic light colors(red, orange and green) can be detected and the background is changed into gray image. Next, we proposed the algorithm which can detect the area of traffic lights in the various surroundings with the wavelet transformation algorithm. Also, we proposed traffic lights recognition algorithm using between the edge operator and the Hausdorff distance algorithm based on CBIR(Content-based Image retrieval). Therefore, the proposed algorithm is more superior to the conventional algorithm by experimenting with the illumination including the traffic lights and the backgrounds with various images.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1122-1129
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• 2015

This paper suggests a method of detecting traffic lights for vehicles by combining the HSV(hue saturation value) color model, morphometric characteristics, and location information appearing on consecutive images in daytime. In order to detect the traffic light, the color corresponding to the signal lights should be explored. It is difficult to detect traffic lights among colors of lights from buildings, taillight of cars, leaves, placards, etc. The proposed algorithm searches for the traffic lights from many candidates using morphometric characteristics and location information in consecutive images. The recognition process is divided into three steps. The first step is to detect candidates after converting RGB channel into HSV color model. The second step is to extract the boundaries between the housing of traffic lights and background by exploiting the assumption that the housing has lower brightness than the surrounding background. The last step is to recognize the signal light after eliminating the false candidates using morphometric characteristics and location information appearing on consecutive images. This paper demonstrates successful detection results of traffic lights from various images captured on the city roads.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.45-50
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• 2022

The vehicle motion in urban environment is determined by surrounding traffic flow, which cause understanding the flow to be a factor that dominantly affects the motion planning of the vehicle. The traffic flow in this urban environment is accessed using various urban infrastructure information. This paper represents a color recognition algorithm for traffic lights to perceive traffic condition which is a main information among various urban infrastructure information. Deep learning based vision open source realizes positions of traffic lights around the host vehicle. The data are processed to input data based on whether it exists on the route of ego vehicle. The colors of traffic lights are estimated through pixel values from the camera image. The proposed algorithm is validated in intersection situations with traffic lights on the test track. The results show that the proposed algorithm guarantees precise recognition on traffic lights associated with the ego vehicle path in urban intersection scenarios.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.132-142
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• 2014

In this paper, we propose a traffic lights detection method using visual attention and spot-lights detection. To detect traffic lights in city streets at day and night time, the proposed method is used the structural form of a traffic lights such as colors, intensity, shape, textures. In general, traffic lights are installed at a position to increase the visibility of the drivers. The proposed method detects the candidate traffic lights regions using the top-down visual saliency model and spot-lights detect models. The visual saliency and spot-lights regions are positions of its difference from the neighboring locations in multiple features and multiple scales. For detecting traffic lights, by not using a color thresholding method, the proposed method can be applied to urban environments of variety changes in illumination and night times.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.67-72
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• 2019

The self-driving cars identify appropriate navigation paths and obstacles to arrive at their destinations without human control. The autonomous cars are capable of sensing driving environments to improve driver and pedestrian safety by sharing with neighbor traffic infrastructure. In this paper, we have focused on pedestrian protection and have designed an improved localization algorithm to track mobile users on roads by interacting with smart traffic lights in vehicle environments. We developed smart traffic lights with the RSSI sensor and built the proposed method by improving the Kalman filter algorithm to localize mobile users accurately. We successfully evaluated the proposed algorithm to improve the mobile user localization with deployed five smart traffic lights.

• Journal of the Semiconductor & Display Technology
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• v.15 no.2
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• pp.43-48
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• 2016

In this paper, we propose a traffic light detection and recognition (TLDR) algorithm in the daytime. The proposed algorithm utilizes the color and shape information for the TLDR. At first, a traffic light is detected and recognized based on its shape information. Then, the color range of the detected traffic light is investigated in HSV color space. The input data of the proposed TLDR algorithm is the color image captured using the black box camera during driving. Our simulations demonstrate that the proposed algorithm can achieve a high detection and recognition performance for the images including traffic lights.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.34-52
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• 2019

This paper proposes an intelligent system performing an application with assistance of an Internet of Things (IoT) platform to control a traffic lights system. In our proposed systems, the traffic lights can be remotely controlled through the Internet. Based on IoT platform, the traffic conditions at different intersections of roads are collected and the traffic lights are controlled in a central manner. For the software part, the algorithm is designed based on the green wave theory to maximize the green bandwidth of arterial roads while addressing a challenging issue: the rapid changes of parameters including cycle time, splits, offset, non-fixed vehicles' velocities and traffic flow along arterial roads. The issue typically happens at some areas where the transportation system is not well organized like in Vietnam. For the hardware part, PLC S7-1200 are placed at the intersections for two purposes: to control traffic lights and to collect the parameters and transmit to a host machine at the operation center. For the communication part, the TCP/IP protocol can be done using a Profinet port embedded in the PLC. Some graphical user interface captures are also presented to illustrate the operation of our proposed system.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.2
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• pp.214-224
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• 2016

This paper proposes an algorithm to effectively detect the traffic lights and recognize the traffic signals using a monocular camera mounted on the front windshield glass of a vehicle in day time. The algorithm consists of three main parts. The first part is to generate the candidates of a traffic light. After conversion of RGB color model into HSI and YCbCr color spaces, the regions considered as a traffic light are detected. For these regions, edge processing is applied to extract the borders of the traffic light. The second part is to divide the candidates into traffic lights and non-traffic lights using Haar-like features and Adaboost algorithm. The third part is to recognize the signals of the traffic light using a template matching. Experimental results show that the proposed algorithm successfully detects the traffic lights and recognizes the traffic signals in real time in a variety of environments.