Headlamp Luminous Intensity Matrix Adjustment Factors for Modeling Traffic Sign Performance

2005 ◽  
Author(s):  
H. Gene Hawkins ◽  
Paul J. Carlson ◽  
Susan T. Chrysler
Keyword(s):  
Luminous Intensity ◽  
Traffic Sign ◽  
Intensity Matrix ◽  
Adjustment Factors

Estimation of adjustment factors for 3X milking

2001 ◽  
Author(s):  
Nerilson Terra Santos
Keyword(s):  
Adjustment Factors

Identification of Degraded Traffic Sign Symbols Using PNN

2011 ◽  
Vol 30 (7) ◽  
pp. 1703-1707 ◽  
Author(s):  
Lun-bo Li ◽  
Guang-fu Ma
Keyword(s):  
Traffic Sign

Convolution Neural Networks for Traffic Sign Classification

2019 ◽  
Author(s):  
Rajashekar A ◽  
Shruti Hegdekar ◽  
Dikpal Shrestha ◽  
Prabin Nepal ◽  
Sujanb Neupane
Keyword(s):  
Neural Networks ◽  
Traffic Sign ◽  
Convolution Neural Networks ◽  
Traffic Sign Classification

Road Traffic Sign Identification in Weak Illumination for Intelligent Vehicle Based on Machine Vision

2018 ◽  
Vol 11 (2) ◽  
pp. 127-134 ◽  
Author(s):  
Zhaofei Huang ◽  
Shian Qiu ◽  
Jun Li ◽  
Yibing Zhao ◽  
Pan Cui ◽  
...  
Keyword(s):  
Machine Vision ◽  
Road Traffic ◽  
Intelligent Vehicle ◽  
Traffic Sign

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

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