An efficient automatic traffic sign detection and recognition method for smartphones

2017 ◽  
Author(s):  
Po-Cheng Shih ◽  
Chi-Yi Tsai ◽  
Chun-Fei Hsu
Keyword(s):  
Recognition Method ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

scholarly journals AUTOMATIC TRAFFIC SIGN DETECTION AND RECOGNITION USING MOBILE LIDAR DATA WITH DIGITAL IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 599-603
Author(s):  
H. Guan ◽  
Y. Yu ◽  
D. Li ◽  
J. Li
Keyword(s):  
High Performance ◽  
Digital Images ◽  
Point Clouds ◽  
Lidar Data ◽  
Recognition Method ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Abstract. This paper presents a traffic sign detection and recognition method from mobile LiDAR data and digital images for intelligent transportation-related applications. The traffic sign detection and recognition method includes two steps: traffic sign interest regions are first extracted from mobile LiDRA data. Next, traffic signs are identified from digital images simultaneously collected from the multi-sensor mobile LiDAR systems via a convolutional capsule network model. The experimental results demonstrate that the proposed method obtains a promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.

scholarly journals Traffic Sign Detection Based on Convolutional Neural Network

2021 ◽  
Vol 9 (8) ◽  
pp. 2926-2934
Author(s):  
Arjun Dileep
Keyword(s):  
Recognition Rate ◽  
Vital Role ◽  
Motive Force ◽  
Image Process ◽  
Traffic Sign ◽  
The Road ◽  
Sign Detection ◽  
Help Systems ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Abstract: In today's world, nearly everything we have a tendency to do has been simplified by machine-driven tasks. In a trial to specialize in the road whereas driving, drivers usually miss out on signs on the facet of the road, that can be dangerous for them and for the folks around them. This drawback may be avoided if there was AN economical thanks to inform the motive force while not having them to shift their focus. Traffic Sign Detection and Recognition (TSDR) plays a vital role here by detection and recognizing a symptom, therefore notifying the motive force of any coming signs. This not solely ensures road safety, however additionally permits the motive force to be at very little a lot of ease whereas driving on tough or new roads. Another normally long-faced drawback isn't having the ability to know the which means of the sign. With the assistance of this Advanced Driver help Systems (ADAS) application, drivers can not face the matter of understanding what the sign says. during this paper, we have a tendency to propose a way for Traffic Sign Detection and Recognition exploitation image process for the detection of a symptom and a Convolutional Neural Networks (CNN) for the popularity of the sign. CNNs have a high recognition rate, therefore creating it fascinating to use for implementing varied laptop vision tasks. TensorFlow is employed for the implementation of the CNN. Keywords: actitvity recognition; knowledge collection; knowledge preprocessing; coaching CNN model ;evaluating model; predicting the result.

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