LED Traffic Sign Detection Using Rectangular Hough Transform

Bae, GiYeong; Ha, JeongMok; Jeon, Jea Young; Jo, Sung Yong; Jeong, Hong

doi:10.1109/icisa.2014.6847422

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…Ohlander et al [20] also used a recursive region splitting method to achieve colour segmentation and applied Hue, Saturation, Intensity (HSI), and sign area. In terms of shape-based recognition, studies used the basic sign shapes of circles, triangles, or rectangles [9,11,[21][22][23][24][25][26]. is method does overcome brightness issues found with colour-based methods.…”

Section: Literature Reviewmentioning

confidence: 99%

The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance

Seraj

Rosales-Castellanos

Shalkamy

et al. 2021

Journal of Advanced Transportation

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Automatic recognition of traffic signs in complex, real-world environments has become a pressing research concern with rapid improvements of smart technologies. Hence, this study leveraged an industry-grade object detection and classification algorithm (You-Only-Look-Once, YOLO) to develop an automatic traffic sign recognition system that can identify widely used regulatory and warning signs in diverse driving conditions. Sign recognition performance was assessed in terms of weather and reflectivity to identify the limitations of the developed system in real-world conditions. Furthermore, we produced several editions of our sign recognition system by gradually increasing the number of training images in order to account for the significance of training resources in recognition performance. Analysis considering variable weather conditions, including fair (clear and sunny) and inclement (cloudy and snowy), demonstrated a lower susceptibility of sign recognition in the highly trained system. Analysis considering variable reflectivity conditions, including sheeting type, lighting conditions, and sign age, showed that older engineering-grade sheeting signs were more likely to go unnoticed by the developed system at night. In summary, this study incorporated automatic object detection technology to develop a novel sign recognition system to determine its real-world applicability, opportunities, and limitations for future integration with advanced driver assistance technologies.

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Section: Literature Reviewmentioning

confidence: 99%

The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance

Seraj

Rosales-Castellanos

Shalkamy

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Shape-based method is the second class; compared with the first class, it is more robust to brightness variation. Hough transforms are applied to detect matrix-shaped LED traffic signs, 7 circular marks 8,9 and triangle signs. 10 However, the performance of shape-based methods degrades significantly in the case of rotation change.…”

Section: Introductionmentioning

confidence: 99%

Deep learning–based traffic sign recognition for unmanned autonomous vehicles

Zang

Wei

Bao

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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Being one of the key techniques for unmanned autonomous vehicle, traffic sign recognition is applied to assist autopilot. Colors are very important clues to identify traffic signs; however, color-based methods suffer performance degradation in the case of light variation. Convolutional neural network, as one of the deep learning methods, is able to hierarchically learn high-level features from the raw input. It has been proved that convolutional neural network-based approaches outperform the color-based ones. At present, inputs of convolutional neural networks are processed either as gray images or as three independent color channels; the learned color features are still not enough to represent traffic signs. Apart from colors, temporal constraint is also crucial to recognize video-based traffic signs. The characteristics of traffic signs in the time domain require further exploration. Quaternion numbers are able to encode multi-dimensional information, and they have been employed to describe color images. In this article, we are inspired to present a quaternion convolutional neural network-based approach to recognize traffic signs by fusing spatial and temporal features in a single framework. Experimental results illustrate that the proposed method can yield correct recognition results and obtain better performance when compared with the state-of-the-art work.

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Real Time Traffic Sign Recognition Algorithm Based on SG-YOLO

Wang

Sun

et al. 2022

Communications in Computer and Information Science

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LED Traffic Sign Detection Using Rectangular Hough Transform

Cited by 6 publications

References 5 publications

The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance

The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance

Deep learning–based traffic sign recognition for unmanned autonomous vehicles

Real Time Traffic Sign Recognition Algorithm Based on SG-YOLO

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