• 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 Auto-vehicle Safety Association
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• v.14 no.4
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• pp.6-15
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• 2022

In this study, a traffic light recognition algorithm was implemented and validated for low-speed special purpose vehicles in an urban environment. Real-time image data using a camera and YOLO algorithm were applied. Two methods were presented to increase the accuracy of the traffic light recognition algorithm, and it was confirmed that the second method had the higher accuracy according to the traffic light type. In addition, it was confirmed that the optimal YOLO algorithm was YOLO v5m, which has over 98% mAP values and higher efficiency. In the future, it is thought that the traffic light recognition algorithm can be used as a dual system to secure the platform safety in the traffic information error of C-ITS.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.751-756
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• 2018

This research extracted traffic light from input video, recognized colors of traffic light, and suggested traffic light color recognizing algorithm applicable to manless autonomous vehicle or ITS by distinguishing signs. To extract traffic light, suggested algorithm extracted the outline with CEA(Canny Edge Algorithm), and applied HCT(Hough Circle Transform) to recognize colors of traffic light and improve the accuracy. The suggested method was applied to the video of stream acquired on the road. As a result, excellent rate of traffic light recognition was confirmed. Especially, ROI including traffic light in input video was distinguished and computing time could be reduced. In even area similar to traffic light, circle was not extracted or V value is low in HSV space, so it's failed in candidate area. So, accuracy of recognition rate could be improved.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.48-54
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• 2023

Nowadays, a blind man finds it very difficult to cross the roads. They should be very vigilant with every step they take. To resolve this problem, Convolutional Neural Networks(CNN) is a best method to analyse the data and automate the model without intervention of human being. In this work, a traffic signal recognition system is designed using CNN for the visually impaired. To provide a safe walking environment, a voice message is given according to light state and timer state at that instance. The developed model consists of two phases, in the first phase the CNN model is trained to classify different images captured from traffic signals. Common Objects in Context (COCO) labelled dataset is used, which includes images of different classes like traffic lights, bicycles, cars etc. The traffic light object will be detected using this labelled dataset with help of object detection model. The CNN model detects the color of the traffic light and timer displayed on the traffic image. In the second phase, from the detected color of the light and timer value a text message is generated and sent to the text-to-speech conversion model to make voice guidance for the blind person. The developed traffic light recognition model recognizes traffic light color and countdown timer displayed on the signal for safe signal crossing. The countdown timer displayed on the signal was not considered in existing models which is very useful. The proposed model has given accurate results in different scenarios when compared to other models.

• 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.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.92-98
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• 2022

This paper proposes a new method of the traffic signal detection and the recognition in an HSI color model. The proposed method firstly converts a ROI image in the RGB model to in the HSI model to segment the color of a traffic signal. Secondly, the segmented colors are dilated by the morphological processing to connect the traffic signal light and the signal light case and finally, it extracts the traffic signal light and the case by the aspect ratio using the connected component analysis. The extracted components show the detection and the recognition of the traffic signal lights. The proposed method is implemented using C language in Raspberry Pi 4 system with a camera module for a real-time image processing. The system was fixedly installed in a moving vehicle, and it recorded a video like a vehicle black box. Each frame of the recorded video was extracted, and then the proposed method was tested. The results show that the proposed method is successful for the detection and the recognition of traffic signals.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.207-214
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• 2010

A method of the region extraction and recognition of a traffic light and speed sign board in the real road environment is proposed. Traffic light was recognized by using brightness and color information based on HSI color model. Speed sign board was extracted by measuring red intensity from the HSI color information We improve the recognition rate by performing an incline compensation of the speed sign for directions clockwise and counterclockwise. The proposed algorithm shows a robust recognition rate in the image sequence which includes traffic light and speed sign board.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1901-1912
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• 2017

Traffic lights at night are usually framed in the image as bright regions bigger than the real size due to glow effect. Moreover, the colors of lighting region saturate to white. So it is difficult to distinguish between different traffic lights at night. Many related studies have tried to decrease the glow effect in the process of capturing images. Some studies drastically decreased the shutter time of the camera to reduce the adverse effect by the glow. However, this makes the video too dark. This study proposes a new idea which utilizes the glow effect. It examines the outer radial region of traffic light. It presents an algorithm to discriminate the color of traffic light by the analysis of the outer radial region. The advantage of the proposed method is that it can recognize traffic lights in the image captured by an ordinary black box camera. Experimental results using seven short videos show the performance of traffic light recognition reporting the precision of 96.4% and the recall of 98.2%. These results show that the proposed method is valid and effective.

• 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.

• 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.