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

2017 ◽  
Author(s):  
Ziya Ozcelik ◽  
Canan Tastimur ◽  
Mehmet Karakose ◽  
Erhan Akin
Keyword(s):  
Intelligent Vehicles ◽  
Traffic Light ◽  
Light Detection ◽  
Detection And Recognition

scholarly journals Prototyping a Traffic Light Recognition Device with Expert Knowledge

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 278 ◽  
Author(s):  
Thiago Almeida ◽  
Hendrik Macedo ◽  
Leonardo Matos ◽  
Nathanael Vasconcelos
Keyword(s):  
Expert Knowledge ◽  
Support Vector ◽  
Background Suppression ◽  
Processing Unit ◽  
Driver Assistance ◽  
Traffic Light ◽  
Accuracy Rate ◽  
Light Detection ◽  
Vector Machines ◽  
Detection And Recognition

Traffic light detection and recognition (TLR) research has grown every year. In addition, Machine Learning (ML) has been largely used not only in traffic light research but in every field where it is useful and possible to generalize data and automatize human behavior. ML algorithms require a large amount of data to work properly and, thus, a lot of computational power is required to analyze the data. We argue that expert knowledge should be used to decrease the burden of collecting a huge amount of data for ML tasks. In this paper, we show how such kind of knowledge was used to reduce the amount of data and improve the accuracy rate for traffic light detection and recognition. Results show an improvement in the accuracy rate around 15%. The paper also proposes a TLR device prototype using both camera and processing unit of a smartphone which can be used as a driver assistance. To validate such layout prototype, a dataset was built and used to test an ML model based on adaptive background suppression filter (AdaBSF) and Support Vector Machines (SVMs). Results show 100% precision rate and recall of 65%.

Traffic light detection and recognition based on Haar-like features

2018 ◽  
Author(s):  
Sang-Hyuk Lee ◽  
Jung-Hawn Kim ◽  
Yong-Jin Lim ◽  
Joonhong Lim
Keyword(s):  
Traffic Light ◽  
Light Detection ◽  
Detection And Recognition

Traffic light detection and recognition for autonomous vehicles

2015 ◽  
Vol 22 (1) ◽  
pp. 50-56 ◽  
Author(s):  
Guo Mu ◽  
Zhang Xinyu ◽  
Li Deyi ◽  
Zhang Tianlei ◽  
An Lifeng
Keyword(s):  
Autonomous Vehicles ◽  
Traffic Light ◽  
Light Detection ◽  
Detection And Recognition

scholarly journals Traffic Light and Arrow Signal Recognition Based on a Unified Network

2021 ◽  
Vol 11 (17) ◽  
pp. 8066
Author(s):  
Tien-Wen Yeh ◽  
Huei-Yung Lin ◽  
Chin-Chen Chang
Keyword(s):  
High Performance ◽  
Focal Length ◽  
Computation Time ◽  
Urban Traffic ◽  
Signal Recognition ◽  
Traffic Light ◽  
Light Detection ◽  
Traffic Lights ◽  
Map Data ◽  
Detection And Recognition

We present a traffic light detection and recognition approach for traffic lights that utilizes convolutional neural networks. We also introduce a technique for identifying arrow signal lights in multiple urban traffic environments. For detection, we use map data and two different focal length cameras for traffic light detection at various distances. For recognition, we propose a new algorithm that combines object detection and classification to recognize the light state classes of traffic lights. Furthermore, we use a unified network by sharing features to decrease computation time. The results reveal that the proposed approach enables high-performance traffic light detection and recognition.

scholarly journals Traffic Lights Detection and Recognition Method Based on the Improved YOLOv4 Algorithm

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 200
Author(s):  
Qingyan Wang ◽  
Qi Zhang ◽  
Xintao Liang ◽  
Yujing Wang ◽  
Changyue Zhou ◽  
...  
Keyword(s):  
Gaussian Model ◽  
Intelligent Vehicles ◽  
Traffic Light ◽  
Data Set ◽  
Traffic Lights ◽  
Enhancement Mechanism ◽  
Feature Enhancement ◽  
Bounding Box ◽  
Prediction Mechanism ◽  
Detection And Recognition

For facing of the problems caused by the YOLOv4 algorithm’s insensitivity to small objects and low detection precision in traffic light detection and recognition, the Improved YOLOv4 algorithm is investigated in the paper using the shallow feature enhancement mechanism and the bounding box uncertainty prediction mechanism. The shallow feature enhancement mechanism is used to extract features from the network and improve the network’s ability to locate small objects and color resolution by merging two shallow features at different stages with the high-level semantic features obtained after two rounds of upsampling. Uncertainty is introduced in the bounding box prediction mechanism to improve the reliability of the prediction of the bounding box by modeling the output coordinates of the prediction bounding box and adding the Gaussian model to calculate the uncertainty of the coordinate information. The LISA traffic light data set is used to perform detection and recognition experiments separately. The Improved YOLOv4 algorithm is shown to have a high effectiveness in enhancing the detection and recognition precision of traffic lights. In the detection experiment, the area under the PR curve value of the Improved YOLOv4 algorithm is found to be 97.58%, which represents an increase of 7.09% in comparison to the 90.49% score gained in the Vision for Intelligent Vehicles and Applications Challenge Competition. In the recognition experiment, the mean average precision of the Improved YOLOv4 algorithm is 82.15%, which is 2.86% higher than that of the original YOLOv4 algorithm. The Improved YOLOv4 algorithm shows remarkable advantages as a robust and practical method for use in the real-time detection and recognition of traffic signal lights.

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