Detection of traffic signs in real-world images: The German traffic sign detection benchmark

Houben, Sebastian; Stallkamp, Johannes; Salmen, Jan; Schlipsing, Marc; Igel, Christian

doi:10.1109/ijcnn.2013.6706807

Cited by 692 publications

(366 citation statements)

References 28 publications

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“…To assess the accuracy of the detection algorithm, the German annotated image data base containing images with road signs was used [4]. It contains more than 50 000 images of road signs, registered under different conditions.…”

Section: Resultsmentioning

confidence: 99%

“…The experiments showed 97.3% correctly localized and classified prohibiting and warning road signs. Table 1 shows the results of accuracy and speed of the algorithms in [4] and the method described in this article. 70,33 % 20 The accuracy of the algorithms listed in the table was obtained using the annotated image database GTSDB [4].…”

Section: Resultsmentioning

confidence: 99%

“…This algorithm shows good results in combination with the localization step. In order to train and test the developed algorithms, we used the German Traffic Sign Detection Database [4]. Fig.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Road Signs Recognition Using Mobile Gpu

Yakimov

2016

Collection of Selected Papers of the II International Conference on Information Technology and Nanotechnology

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Abstract. This article shows an effective implementation of the algorithm for detection of road signs using video obtained by a camera installed in a vehicle. Road signs detection and recognition are implemented using CUDA and operate in real-time on a mobile GPU. Vehicle speed is used to predict the position of road signs in the adjacent frames in a video sequence. The experimental results confirmed the high efficiency of the developed road signs system.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Real-Time Road Signs Recognition Using Mobile Gpu

Yakimov

2016

Collection of Selected Papers of the II International Conference on Information Technology and Nanotechnology

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“…To assess the performance of the proposed algorithm, the German Traffic Sign Detection Benchmark (GTSRB) [10] dataset was used. The GTSRB contains 40 types of road signs, including eight types of speed signs.…”

Section: Resultsmentioning

confidence: 99%

“…A CNN learns the multiple stages of invariant features using a combination of supervised and unsupervised learning. Although CNN-based method showed a higher classification rate based on the German Traffic Sign Recognition Benchmark (GTSRB) [10], a detailed algorithm for detecting traffic signs on a real road and incorporating the detection into the classifier in real time was not proposed [6]. Unlike CNN-based methods, Gim et al [6] used a two-class boosted random forest with low-dimensional oriented center symmetric-local binary patterns by changing original local binary patterns (LBP).…”

Section: ⅰ Introductionmentioning

confidence: 99%

Real-time Traffic Sign Recognition using Rotation-invariant Fast Binary Patterns

Hwang¹,

Ko²,

Nam³

2016

Journal of Broadcast Engineering

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In this paper, we focus on recognition of speed-limit signs among a few types of traffic signs because speed-limit sign is closely related to safe driving of drivers. Although histogram of oriented gradient (HOG) and local binary patterns (LBP) are representative features for object recognition, these features have a weakness with respect to rotation, in that it does not consider the rotation of the target object when generating patterns. Therefore, this paper propose the fast rotation-invariant binary patterns (FRIBP) algorithm to generate a binary pattern that is robust against rotation. The proposed FRIBP algorithm deletes an unused layer of the histogram, and eliminates the shift and comparison operations in order to quickly extract the desired feature. The proposed FRIBP algorithm is successfully applied to German Traffic Sign Recognition Benchmark (GTSRB) datasets, and the results show that the recognition capabilities of the proposed method are similar to those of other methods. Moreover, its recognition speed is considerably enhanced than related works as approximately 0.47second for 12,630 test data.

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R‐ICTS: Recognize the Indian cautionary traffic signs in real‐time using an optimized adaptive boosting cascade classifier and a convolutional neural network

Satti

Devi

Srinivasan

2021

Concurrency and Computation

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Cautionary traffic signs are of immense significance to traffic safety. In this study, a robust and optimal real-time approach to recognize the Indian cautionary traffic signs (ICTS) is proposed. ICTS are all triangles with a white backdrop, a red border, and a black pattern. A dataset of 34,000 real-time images has been acquired under various environmental conditions and categorized into 40 distinct classes. Pre-processing techniques are used to transform RGB images to gray-scale images and enhance contrast in images for superior performance. To find the ICTS, an optimized adaptive boosting cascade classifier is used. To classify the specific category of signs found by the optimized adaptive boosting cascade classifier, an 11-layer CNN model is built. Finally, using computer vision methods, this model is tested in real-time. Evaluation metrics such as precision, recall, F1 measure, error rate, and mAP are expressed on the ICTS, GTSDB, LISA, STSD, and DITS-based datasets to evaluate the proposed method and compare the results of predictions with other investigations. When compared to other state-of-the-art objects detection models such as SSD, YOLOv3, and Faster RCNN, the proposed model outperformed them all, with a precision of 97.15%, recall rate of 96.74%, an error rate of 3.26%, f 1 -score of 96.94%, and

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Detection of traffic signs in real-world images: The German traffic sign detection benchmark

Cited by 692 publications

References 28 publications

Real-Time Road Signs Recognition Using Mobile Gpu

Real-Time Road Signs Recognition Using Mobile Gpu

Real-time Traffic Sign Recognition using Rotation-invariant Fast Binary Patterns

R‐ICTS: Recognize the Indian cautionary traffic signs in real‐time using an optimized adaptive boosting cascade classifier and a convolutional neural network

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