A vision based traffic light detection and recognition approach for intelligent vehicles

Ozcelik, Ziya; Taştimur, Canan; Karaköse, Mehmet; Akın, Erhan

doi:10.1109/ubmk.2017.8093430

Cited by 22 publications

(7 citation statements)

References 17 publications

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“…These vary with the techniques used, with regard to the environment and the cars to be used. Ozcelik et al proposed A Vision Based Traffic Light Detection and Intelligent Vehicle Recognition Approach (Ozcelik, 2017). Images are taken using a camera, and processing to detect the traffic is performed stepwise.…”

Section: Related Workmentioning

confidence: 99%

Detection and Localization of Traffic Lights Using Yolov3 and Stereo Vision

Omar

Lee

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This paper focus on traffic light distance measurement using stereo camera which is a very important and challenging task in image processing domain, where it is used in several systems such as Driving Safety Support Systems (DSSS), autonomous driving and traffic mobility. In this paper, we propose an integrated traffic light distance measurement system for self-driving based on stereo image processing. Therefore, an algorithm to spatially locate the detected traffic light is required in order to make these detections useful. In this paper, an algorithm to detect, classify the traffic light colours and spatially locate traffic light are integrated. Detection and colours classification are made simultaneously via YOLOv3, using RGB images. 3D traffic light localization is achieved by estimating the distance from the vehicle to the traffic light, by looking at detector 2D bounding boxes and the disparity map generated by stereo camera. Moreover, Gaussian YOLOv3 weights based on KITTI and Berkeley datasets has been replaced with the COCO dataset. Therefore, a detection algorithm that can cope with mislocalizations is required in autonomous driving applications. This paper proposes an integrated method for improving the detection accuracy and traffic lights colours classification while supporting a real-time operation by modelling the bounding box (bbox) of YOLOv3. The obtained results show fair results within 20 meters away from the sensor, while misdetection and classification appeared in further distance.

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Section: Related Workmentioning

confidence: 99%

Detection and Localization of Traffic Lights Using Yolov3 and Stereo Vision

Omar

Lee

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…It is carried out to get rid little conceivable elements in the background by removing some pixel that is not important in erosion process as in (6). Dilation process will add some pixels to the pixels left in the image after going through the erosion process as in (7). This process will re-establish the original size of the blobs and make them more compact.…”

Section: B Image Processingmentioning

confidence: 99%

“…It may also help people with color blind disabilities and the elderly [6]. Previous research used color space conversion to Hue, Saturation Value (HSV) [7], binarization and morphology features filtering methods in the preprocessing stage. However, there are some improvements in later where they use image segmentation, morphology process and threshold segmentation in YCbCr color space [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Traffic Light Detection and Interpretation using Circle Centroid

2019

IJITEE

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The paper proposes a new method to recognize the sequence of a traffic light using image processing algorithm. Invariant in factor lightning and weather condition that lead to misinterpret the color of traffic light is one of the factors of accident at traffic light conjunction besides the behavior of the driver itself. The process to identify the color and shape of traffic light are Image Acquisition, Pre-Processing, Detection, Feature Extraction and Interpretation. RGB normalization is performed and simple thresholding method that acts as color segmentation provides a better division of the traffic light colors. Circle Hough Transform and HSV color features based on the traffic light aspect are used to decide whether the spots on the frames are likely to be traffic lights’ color and shape. The detection of traffic light will be obtained after identifying the feature such as the centroid of the Circle Hough Transform that need to be extracted at the end of the result. The research has been improved by focusing on detection and interpretation of traffic light based on real time video rather than image sample as the input. The proposed algorithm can detect the green color accurately with maximum accuracy of 83.8%, yellow color of 75.6% and red color with minimum accuracy of 70.19%. This indicates that there is a possibility to use the proposed algorithm to detect the three different color of green, yellow and red color of traffic lights’ colour.

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“…Detection and Recognition Approach for Intelligent Vehicles [7]. Images are taken using a camera and processing is performed step wise to detect the traffic.…”

Section: Ozcelik Et Al Have Proposed a Vision Based Traffic Lightmentioning

confidence: 99%

“…But these methodologies are also hampered due to the presence of various drawbacks. The systems presented in [1], [4], [7], [8], [10], [11] and [12] fail in considering a vast variety of both Training and Testing datasets, that can be considered to scale the respective systems. [3] and [6] fail on being tested in harsh weather conditions.…”

Section: Behrendt Et Al Have Proposed a Deep Learning Approach Tomentioning

confidence: 99%

Real Time Traffic Light Detection by Autonomous Vehicles using Artificial Neural Network Techniques

Mahesh¹,

T²

2019

IJITEE

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Autonomous vehicles are the reality of the future, they will open up the way for future advanced systems where computers are expected to take over the decision making of driving. These automobiles are capable of sensing their environment and moving with little or no human input. The main goal of this research is to detect traffic light in real-time for autonomous vehicles. Apart from taking decisions to navigate in the right manner the autonomous vehicles important task is to detect traffic lights, so that it can obey the traffic rules with sufficient precision. The work carried out in this research makes use of two Artificial Intelligence technique, these techniques are compared in accomplishing the task of traffic light detection in real time. The two models that are designed and implemented are Convolution neural network (CNN) and Deep Convolution Inverse Graphics Network (DCIGN). The results clearly show that DCIGN out performance CNN by 8%.

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A vision based traffic light detection and recognition approach for intelligent vehicles

Cited by 22 publications

References 17 publications

Detection and Localization of Traffic Lights Using Yolov3 and Stereo Vision

Detection and Localization of Traffic Lights Using Yolov3 and Stereo Vision

Real-Time Traffic Light Detection and Interpretation using Circle Centroid

Real Time Traffic Light Detection by Autonomous Vehicles using Artificial Neural Network Techniques

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