Fast recognition algorithm for static traffic sign information

Guo, Shaocui; Yang, Xu

doi:10.1515/phys-2018-0135

Cited by 3 publications

(2 citation statements)

References 10 publications

(10 reference statements)

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“…Liu [ 40 ] analyzed the relationship between the information quantity of urban roadside traffic signs and drivers’ visibility based on information transmission. Guo [ 41 ] proposed a fast recognition algorithm based on speeded-up robust features (SURF) for static traffic sign information of highways. Lyu [ 42 ] revealed that the workload is highly related to the amount of information on traffic signs, and reaction time increases with the information grade while driving experience and gender are not significant.…”

Section: Literature Reviewmentioning

confidence: 99%

Setting method of exit advance guide signs in mountainous expressway tunnel based on information quantization theory

Shang

Wu²,

et al. 2023

PLoS ONE

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Driving behavior in expressway tunnels is more complicated than in common roadbed sections because of differences in illuminance, visual range, speed perception, and reaction time. To explore the setting method of exit advance guide signs in expressway tunnels and improve the effectiveness of drivers’ recognition of them, we propose 12 layout forms based on information quantification theory. In experiments, UC-win/Road was used to build a simulation scene, and the recognition reaction time of 12 element combinations of exit advance guide signs of different subjects was collected through an E-Prime simulation experiment. The loading effectiveness of the signs was analyzed based on the subjective workload and comprehensive evaluation scores of different subjects. The results are the following. The width of the exit advance guide sign layout in the tunnel is negatively correlated with the height of Chinese characters and the distance between the characters and the edge of the sign. The larger the height of Chinese characters and the distance between them and the edge of the sign, the smaller the maximum layout width of the sign. Considering the driver’s reaction time, subjective workload, sign recognition, amount of sign information, sign accuracy, and sign safety of 12 different information combinations, we suggest that exit advance guide signs in tunnels should be laid out as Chinese/English place name + distance + guide arrow.

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Section: Literature Reviewmentioning

confidence: 99%

Setting method of exit advance guide signs in mountainous expressway tunnel based on information quantization theory

Shang

Wu²,

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Color space has also been used, for example, in segmentation in HSV [4]. Researchers extracted SURF feature points from signs and used corroding images to match them [5]. In some studies, more complex feature descriptors, such as coarse localization of signs based on the Hough transform [6], were used for sign extraction.…”

Section: Introductionmentioning

confidence: 99%

Traffic Sign Detection and Recognition Using Multi-Frame Embedding of Video-Log Images

Huang

Ying

2023

Remote Sensing

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The detection and recognition of traffic signs is an essential component of intelligent vehicle perception systems, which use on-board cameras to sense traffic sign information. Unfortunately, issues such as long-tailed distribution, occlusion, and deformation greatly decrease the detector’s performance. In this research, YOLOv5 is used as a single classification detector for traffic sign localization. Afterwards, we propose a hierarchical classification model (HCM) for the specific classification, which significantly reduces the degree of imbalance between classes without changing the sample size. To cope with the shortcomings of a single image, a training-free multi-frame information integration module (MIM) was constructed, which can extract the detection sequence of traffic signs based on the embedding generated by the HCM. The extracted temporal detection information is used for the redefinition of categories and confidence. At last, this research performed detection and recognition of the full class on two publicly available datasets, TT100K and ONCE. Experimental results show that the HCM-improved YOLOv5 has a mAP of 79.0 in full classes, which exceeds that of state-of-the-art methods, and achieves an inference speed of 22.7 FPS. In addition, MIM further improves model performance by integrating multi-frame information while only slightly increasing computational resource consumption.

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An Improvement of CNN Model for Traffic Sign Recognition and Classification

Mekhaznia,

Fares

2022

Communications in Computer and Information Science

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Fast recognition algorithm for static traffic sign information

Cited by 3 publications

References 10 publications

Setting method of exit advance guide signs in mountainous expressway tunnel based on information quantization theory

Setting method of exit advance guide signs in mountainous expressway tunnel based on information quantization theory

Traffic Sign Detection and Recognition Using Multi-Frame Embedding of Video-Log Images

An Improvement of CNN Model for Traffic Sign Recognition and Classification

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