Geometrical, Physical and Text/Symbol Analysis Based Approach of Traffic Sign Detection System

2006 ◽  
Author(s):  
Yangxing Liu ◽  
T. Ikenaga ◽  
S. Goto
Keyword(s):  
Detection System ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

scholarly journals Traffic Sign Detection System for Locating Road Intersections and Roundabouts: The Chilean Case

Sensors ◽  
2017 ◽  
Vol 17 (6) ◽  
pp. 1207 ◽  
Author(s):  
Gabriel Villalón-Sepúlveda ◽  
Miguel Torres-Torriti ◽  
Marco Flores-Calero
Keyword(s):  
Detection System ◽  
Traffic Sign ◽  
Sign Detection ◽  
Road Intersections ◽  
Traffic Sign Detection

scholarly journals REAL OBJECT DETECTION SYSTEM FOR IRAQ TRAFFIC SIGNS BASED ON MASK R-CNN

2021 ◽  
Vol 4 (3) ◽  
pp. 12-22
Author(s):  
Ammar A. Aggar ◽  
Mohammed J. Zaiter ◽  
Abdalrazak T. Raheem
Keyword(s):  
Object Detection ◽  
Detection System ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Timely Response ◽  
Sign Detection ◽  
Weak Light ◽  
Real Video ◽  
Important Object ◽  
Traffic Sign Detection

Traffic signs object detection has gained great interest in recent years, as one of the most important object detector applications. Traffic signs detection is based on deep learning, which gives it the benefit of high detection precision and timely response to condition changes of the traffic. Therefore, this paper shows an efficient method for detecting traffic signs in real-time. Hence, it implements a new Iraqi Traffic Sign Detection Benchmark (IQTSDB) dataset based on Mask Region-based Convolutional Neural Network (Mask R-CNN). The results show that the implementation of IQTSDB dataset with Mask R-CNN has a great efficiency in different conditions such as sunny, cloudy, weak light, and rainy conditions. In addition, the real video captured for traffic signs in Baghdad has been taken and compared to the German Traffic Signs Detection Benchmark (GTSDB) dataset. The IQTSDB dataset has a better performance than GTSDB dataset based on the performance parameters training loss and mean Average Precision (mAP).

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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