Towards real-time traffic sign detection and classification

Yang, Yi; Luo, Huiwu; Xu, Haisong; Wu, Fuchao

doi:10.1109/itsc.2014.6957671

Cited by 40 publications

(48 citation statements)

References 18 publications

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“…In this study, we focus on automated traffic sign detection systems and investigate the effect of challenging conditions in algorithmic performance. Currently, the performance of traffic sign detection algorithms are tested with existing traffic sign datasets in the literature [1][2][3][4][5][6][7][8][9][10][11][12], which have been very useful to develop and evaluate state-of-the-art traffic sign recognition and detection algorithms. However, these datasets are usually very limited in terms of type and severity of challenging Authors are with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0250 USA.…”

Section: Traffic Sign Detection Under Challenging Conditions: a Deepementioning

confidence: 99%

“…Based on the recommendations of the committee members and follow-up discussions, we added the remaining challenging conditions including decolorization, codec error, dirty lens, noise, shadow, and haze. All of the challenging conditions other than snow were observed in the prior real-world traffic datasets [1][2][3][4][5][6][7][8][9][10][11][12] as summarized in Table I, which indicates the relevance and significance of these conditions. To simulate challenging conditions, we utilized the state-of-the-art visual effects and motion graphics software Adobe(c) After Effects, which has been commonly used for realistic image and video processing and synthesis in the literature [30][31][32].…”

Section: B Challenging Conditionsmentioning

confidence: 99%

See 1 more Smart Citation

Traffic Sign Detection Under Challenging Conditions: A Deeper Look into Performance Variations and Spectral Characteristics

Temel

Chen

AlRegib

2020

IEEE Trans. Intell. Transport. Syst.

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Traffic signs are critical for maintaining the safety and efficiency of our roads. Therefore, we need to carefully assess the capabilities and limitations of automated traffic sign detection systems. Existing traffic sign datasets are limited in terms of type and severity of challenging conditions. Metadata corresponding to these conditions are unavailable and it is not possible to investigate the effect of a single factor because of simultaneous changes in numerous conditions. To overcome the shortcomings in existing datasets, we introduced the CURE-TSD-Real dataset, which is based on simulated challenging conditions that correspond to adversaries that can occur in real-world environments and systems. We test the performance of two benchmark algorithms and show that severe conditions can result in an average performance degradation of 29% in precision and 68% in recall. We investigate the effect of challenging conditions through spectral analysis and show that challenging conditions can lead to distinct magnitude spectrum characteristics. Moreover, we show that mean magnitude spectrum of changes in video sequences under challenging conditions can be an indicator of detection performance. CURE-TSD-Real dataset is available online at https://github.com/olivesgatech/CURE-TSD.

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Section: Traffic Sign Detection Under Challenging Conditions: a Deepementioning

confidence: 99%

Section: B Challenging Conditionsmentioning

confidence: 99%

Traffic Sign Detection Under Challenging Conditions: A Deeper Look into Performance Variations and Spectral Characteristics

Temel

Chen

AlRegib

2020

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…Such as [8] exploits the CIECAM color appearance model and propose to calculate different color attributes including lightness, chroma and hue angle for traffic sign segmentation. The color probability model (CPM) proposed by [9] estimates the color distribution of traffic signs from the training samples, and the Ohta space [10] is used rather than RGB space. Besides the color-based methods, shape-based detection algorithms are also heavily investigated.…”

Section: Related Workmentioning

confidence: 99%

VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection

Yuan

Xiong

Wang

2019

IEEE Trans. on Image Process.

149

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Although traffic sign detection has been studied for years and great progress has been made with the rise of deep learning technique, there are still many problems remaining to be addressed. For complicated real-world traffic scenes, there are two main challenges. Firstly, traffic signs are usually smallsize objects, which makes it more difficult to detect than large ones; Secondly, it is hard to distinguish false targets which resemble real traffic signs in complex street scenes without context information. To handle these problems, we propose a novel end-to-end deep learning method for traffic sign detection in complex environments. Our contributions are as follows: 1) We propose a multi-resolution feature fusion network architecture which exploits densely connected deconvolution layers with skip connections, and can learn more effective features for small-size object; 2) We frame the traffic sign detection as a spatial sequence classification and regression task, and propose a vertical spatial sequence attention (VSSA) module to gain more context information for better detection performance. To comprehensively evaluate the proposed method, we do experiments on several traffic sign datasets as well as the general object detection dataset and the results have shown the effectiveness of our proposed method.

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“…In [4] and [5], the authors have proposed text-based detection systems for traffic panels that could include information that can vary substantially. Computational complexity of the recognition algorithms plays a significant role for real-time applications since autonomous vehicles are time-critical systems [6], [7]. In [6], the authors have sought to enhance the performance of convolutional neural networks for faster performance in real-time applications through localization of the traffic-signs in the input images based on their types.…”

Section: A Prior Literaturementioning

confidence: 99%

Reliable Smart Road Signs

Sayin

Lin

Kang

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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In this paper, we propose a game theoretical adversarial intervention detection mechanism for reliable smart road signs. A future trend in intelligent transportation systems is "smart road signs" that incorporate smart codes (e.g., visible at infrared) on their surface to provide more detailed information to smart vehicles. Such smart codes make road sign classification problem aligned with communication settings more than conventional classification. This enables us to integrate well-established results in communication theory, e.g., error-correction methods, into road sign classification problem. Recently, vision-based road sign classification algorithms have been shown to be vulnerable against (even) small scale adversarial interventions that are imperceptible for humans. On the other hand, smart codes constructed via error-correction methods can lead to robustness against small scale intelligent or random perturbations on them. In the recognition of smart road signs, however, humans are out of the loop since they cannot see or interpret them. Therefore, there is no equivalent concept of imperceptible perturbations in order to achieve a comparable performance with humans. Robustness against small scale perturbations would not be sufficient since the attacker can attack more aggressively without such a constraint. Under a game theoretical solution concept, we seek to ensure certain measure of guarantees against even the worst case (intelligent) attackers that can perturb the signal even at large scale. We provide a randomized detection strategy based on the distance between the decoder output and the received input, i.e., error rate. Finally, we examine the performance of the proposed scheme over various scenarios.

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Towards real-time traffic sign detection and classification

Cited by 40 publications

References 18 publications

Traffic Sign Detection Under Challenging Conditions: A Deeper Look into Performance Variations and Spectral Characteristics

Traffic Sign Detection Under Challenging Conditions: A Deeper Look into Performance Variations and Spectral Characteristics

VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection

Reliable Smart Road Signs

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