Research on traffic sign detection algorithm based on deep learning

2018 ◽  
Vol 30 (22) ◽  
pp. e4675 ◽  
Author(s):  
Quan Wang ◽  
Weiping Fu
Keyword(s):  
Deep Learning ◽  
Detection Algorithm ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

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.

Traffic sign detection algorithm based on improved Faster R-CNN

2021 ◽  
Vol 36 (3) ◽  
pp. 484-492
Author(s):  
Zhe LI ◽  
◽  
Hui-hui ZHANG ◽  
Jun-yong DENG
Keyword(s):  
Detection Algorithm ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

scholarly journals A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2

Algorithms ◽  
2017 ◽  
Vol 10 (4) ◽  
pp. 127 ◽  
Author(s):  
Jianming Zhang ◽  
Manting Huang ◽  
Xiaokang Jin ◽  
Xudong Li
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

Traffic Sign Detection and Recognition using Deep Learning

2021 ◽  
Author(s):  
Rudri Mahesh Oza ◽  
Angelina Geisen ◽  
Taehyung Wang
Keyword(s):  
Deep Learning ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Korean Traffic Sign Detection Using Deep Learning

2018 ◽  
Author(s):  
Prateek Manocha ◽  
Ayush Kumar ◽  
Jameel Ahmed Khan ◽  
Hyunchul Shin
Keyword(s):  
Deep Learning ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

Research on Traffic Sign Detection Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 3304-3308
Author(s):  
Mei Hua Xu ◽  
Yi Da Liu ◽  
Chen Jun Xia
Keyword(s):  
Clustering Algorithm ◽  
Color Image ◽  
Ccd Camera ◽  
Detection Algorithm ◽  
Traffic Sign ◽  
The Road ◽  
Sign Detection ◽  
Image Separation ◽  
Series Of Experiments ◽  
Traffic Sign Detection

As an important part of Advanced Driver Assistance System (ADAS), the traffic sign detection has been paid more and more attention. This paper studied and implemented a valid algorithm of traffic sign detection. Using K-means clustering algorithm to complete the image separation and extraction of prohibition signs from the RGB color image, and then matching them with templates to realize the detection of traffic signs by SIFT algorithm. Series of experiments for traffic sign detection have been carried out to prove the validity and correctness of the algorithm on the basis of the road images in front of the vehicle collected by CCD camera.

scholarly journals Evaluation Method of Deep Learning-Based Embedded Systems for Traffic Sign Detection

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 101217-101238
Author(s):  
Miguel Lopez-Montiel ◽  
Ulises Orozco-Rosas ◽  
Moises Sanchez-Adame ◽  
Kenia Picos ◽  
Oscar Humberto Montiel Ross
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Evaluation Method ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection
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