A review on automatic detection and recognition of traffic sign

Gudigar, Anjan; Shreesha, C; Raghavendra, U.

doi:10.1007/s11042-014-2293-7

Cited by 92 publications

(56 citation statements)

References 72 publications

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“…Researchers have been proposing all types of solutions such as the ones using hand-crafted features in model-based solutions [9], leveraging simple features in learning-based approaches [10], and, the more recent and state-of-the-art, using deep learning based methods [6], [11] that is the focus of this work. A detailed and complete review of these methods can be found in [12].…”

Section: Introductionmentioning

confidence: 99%

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

Tabelini

Paixão

Berriel

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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Deep learning has been successfully applied to several problems related to autonomous driving. Often, these solutions rely on large networks that require databases of real image samples of the problem (i.e., real world) for proper training. The acquisition of such real-world data sets is not always possible in the autonomous driving context, and sometimes their annotation is not feasible (e.g., takes too long or is too expensive). Moreover, in many tasks, there is an intrinsic data imbalance that most learning-based methods struggle to cope with. It turns out that traffic sign detection is a problem in which these three issues are seen altogether. In this work, we propose a novel database generation method that requires only (i) arbitrary natural images, i.e., requires no real image from the domain of interest, and (ii) templates of the traffic signs, i.e., templates synthetically created to illustrate the appearance of the category of a traffic sign. The effortlessly generated training database is shown to be effective for the training of a deep detector (such as Faster R-CNN) on German traffic signs, achieving 95.66% of mAP on average. In addition, the proposed method is able to detect traffic signs with an average precision, recall and F1-score of about 94%, 91% and 93%, respectively. The experiments surprisingly show that detectors can be trained with simple data generation methods and without problem domain data for the background, which is in the opposite direction of the common sense for deep learning.

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Section: Introductionmentioning

confidence: 99%

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

Tabelini

Paixão

Berriel

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

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“…ese challenges have hindered its successful application in some special scenarios, such as the medical diagnosis tasks [15], where there is lack of training data and automatic driving systems [16] that require high real-time performance.…”

Section: Introductionmentioning

confidence: 99%

A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification

Zheng

Tian

Jiang

et al. 2020

Discrete Dynamics in Nature and Society

147

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Nowadays, deep learning has achieved remarkable results in many computer vision related tasks, among which the support of big data is essential. In this paper, we propose a full stage data augmentation framework to improve the accuracy of deep convolutional neural networks, which can also play the role of implicit model ensemble without introducing additional model training costs. Simultaneous data augmentation during training and testing stages can ensure network optimization and enhance its generalization ability. Augmentation in two stages needs to be consistent to ensure the accurate transfer of specific domain information. Furthermore, this framework is universal for any network architecture and data augmentation strategy and therefore can be applied to a variety of deep learning based tasks. Finally, experimental results about image classification on the coarse-grained dataset CIFAR-10 (93.41%) and fine-grained dataset CIFAR-100 (70.22%) demonstrate the effectiveness of the framework by comparing with state-of-the-art results.

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“…However, due to complexity of influ-ence factors in actual road scene, traffic sign detection and recognition with real time and high accuracy faces a great challenge [2]. So, automatic Traffic Sign Detection and Recognition (ATSDR) with image processing technology are considered as crucial technologies of various intelligent vehicle systems [3].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

Kuang¹,

Fu²,

Liu³

2018

Int. J. Onl. Eng.

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Abstract-Real-time detection and recognition of road traffic signs plays an important role in advanced driving assistance system. Typically, the region of interest (ROI) method is effective in feature extraction but inefficient because it is sensitive to illumination changes. In this paper, we propose a maximally stable extremal regions (MSER) method with image enhancement to greatly improve ROI. Firstly, we employ gray world algorithm to process original images. And then potential areas of traffic signs are obtained through increasing the image contrast ratio and extracting the image-enhanced MSER. According to the characteristic variable and the geometry moment invariants, the geometric characteristics of traffic signs are extracted to obtain the ROIs. Finally, HSV-HOG-LBP feature is constructed and the random forests algorithm is used to identify the traffic signs. The experimental results show that our proposed method show strong robustness on illumination condition and rotation scale, and achieves a good performance by experiments with actual images and German traffic sign detection benchmark (GTSDB) data set.

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A review on automatic detection and recognition of traffic sign

Cited by 92 publications

References 72 publications

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification

Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

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