Road Sign Recognition System Based on Wavelet Transform and OPSA Point Set Distance

2018 ◽  
Author(s):  
Lei Wang
Keyword(s):  
Wavelet Transform ◽  
Recognition System ◽  
Sign Recognition ◽  
Road Sign ◽  
Road Sign Recognition ◽  
Point Set ◽  
Set Distance

scholarly journals Road Sign Recognition System by templat matching

2000 ◽  
Vol 120 (1) ◽  
pp. 174-175
Author(s):  
Satoshi Yatsumonji ◽  
Hisaya Tanaka ◽  
Hideto Ide
Keyword(s):  
Recognition System ◽  
Sign Recognition ◽  
Road Sign ◽  
Road Sign Recognition

Road sign recognition system based on GentleBoost with sharing features

2011 ◽  
Author(s):  
Jin-Yi Wu ◽  
Chien-Chung Tseng ◽  
Chun-Hao Chang ◽  
Jenn-Jier James Lien ◽  
Ju Chin Chen ◽  
...  
Keyword(s):  
Recognition System ◽  
Sign Recognition ◽  
Road Sign ◽  
Road Sign Recognition

scholarly journals Autonomous Driving Robot That Drives and Returns along a Planned Route in Underground Mines by Recognizing Road Signs

2021 ◽  
Vol 11 (21) ◽  
pp. 10235
Author(s):  
Heonmoo Kim ◽  
Yosoon Choi
Keyword(s):  
Machine Vision ◽  
Autonomous Driving ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Underground Mines ◽  
Driving Mode ◽  
Road Signs ◽  
Sign Recognition ◽  
Road Sign ◽  
Road Sign Recognition

In this study, an autonomous driving robot that drives and returns along a planned route in an underground mine tunnel was developed using a machine-vision-based road sign recognition algorithm. The robot was designed to recognize road signs at the intersection of a tunnel using a geometric matching algorithm of machine vision, and the autonomous driving mode was switched according to the shape of the road sign to drive the robot according to the planned route. The autonomous driving mode recognized the shape of the tunnel using the distance data from the LiDAR sensor; it was designed to drive while maintaining a fixed distance from the centerline or one wall of the tunnel. A machine-vision-based road sign recognition system and an autonomous driving robot for underground mines were used in a field experiment. The results reveal that all road signs were accurately recognized, and the average matching score was 979.14 out of 1000, confirming stable driving along the planned route.

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