iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/s00500-021-06726-w
Automatic road sign detection and recognition based on neural network | Soft Computing Skip to main content

Advertisement

Springer Nature Link
Log in

Automatic road sign detection and recognition based on neural network

  • Data analytics and machine learning
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Road sign detection and recognition is an integral part of intelligent transportation systems. It increases protection by reminding the driver of the current condition of the route, such as notices, bans, limitations, and other valuable driving information. This paper describes a novel system for automatic detection and recognition of road signs, which is achieved in two main steps. First, the initial image is pre-processed using DBSCAN clustering algorithm. The clustering is performed based on color information, and the generated clusters are segmented using artificial neural networks (ANN) classifier. The resulting ROIs are then carried out based on their aspect ratio and size to retain only significant ones. Then, a shape-based classification is performed using ANN as classifier and HDSO as feature to detect the circular, rectangular and triangular shapes. Second, a hybrid feature is defined to recognize the ROIs detected from the first step. It involves a combination of the so-called GLBP-Color which is an extension of the classical gradient local binary patterns feature to the RGB color space and the local self-similarity feature. ANN, AdaBoost, and support vector machine have been tested with the introduced hybrid feature and the first one is selected as it outperforms the other two. The proposed method has been tested in outdoor scenes, using a collection of common databases, well known in the traffic sign community (GTSRB, GTSDB, and STS). The results demonstrate the effectiveness of our method when compared to recent state-of-the-art methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Amal Bouti, Adnane Mahraz Med, Jamal Riffi, Hamid Tairi (2019) A robust system for road sign detection and classification using lenet architecture based on convolutional neural network. Soft Comput 7:1–13

    Google Scholar 

  • Bascón S Maldonado, Rodríguez J Acevedo, Arroyo S Lafuente, Caballero A Fernndez, López-Ferreras Francisco (2010) An optimization on pictogram identification for the road-sign recognition task using svms. Comput Vis Image Underst 114(3):373–383

    Article  Google Scholar 

  • Belaroussi Rachid, Foucher Philippe, Tarel Jean-Philippe, Soheilian Bahman, Charbonnier Pierre, Paparoditis Nicolas (2010) Road sign detection in images: a case study. In: 2010 20th international conference on pattern recognition, IEEE, pp 484–488

  • Benallal Mohamed, Meunier Jean (2003) Real-time color segmentation of road signs. In: CCECE 2003-Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No. 03CH37436), vol 3, pp 1823–1826. IEEE

  • Bianco Simone, Mazzini Davide, Pau DP, Schettini Raimondo (2015) Local detectors and compact descriptors for visual search: a quantitative comparison. Digital Signal Process 44:1–13

    Article  Google Scholar 

  • Cireşan Dan, Meier Ueli, Masci Jonathan, Schmidhuber Jürgen (2011) A committee of neural networks for traffic sign classification. In: The 2011 international joint conference on neural networks, IEEE, pp 1918–1921

  • Dalal Navneet, Triggs Bill (2005) Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE computer society conference on, vol 1, IEEE, pp 886–893

  • Daugman John G (1985) Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. JOSA A 2(7):1160–1169

    Article  Google Scholar 

  • El Ansari Mohamed, Masmoudi Lhoussaine, Bensrhair Abdelaziz (2007) A new regions matching for color stereo images. Pattern Recogn Lett 28(13):1679–1687

    Article  Google Scholar 

  • El Ansari Mohamed, Mousset Stéphane, Bensrhair Abdelaziz (2010) Temporal consistent real-time stereo for intelligent vehicles. Pattern Recogn Lett 31(11):1226–1238

    Article  Google Scholar 

  • Ellahyani Ayoub, El Ansari Mohamed (2016) Complementary features for traffic sign detection and recognition. In: 2016 IEEE/ACS 13th international conference of computer systems and applications (AICCSA), IEEE, pp 1–6

  • Ellahyani Ayoub, El Ansari Mohamed (2017) Mean shift and log-polar transform for road sign detection. Multimed Tools Appl 76(22):24495–24513

    Article  Google Scholar 

  • Ellahyani Ayoub, El Ansari Mohamed, El Jaafari Ilyas (2016) Traffic sign detection and recognition based on random forests. Appl Soft Comput 46:805–815

    Article  Google Scholar 

  • Ellahyani Ayoub, El Ansari Mohamed, El Jaafari Ilyas, Charfi Said (2016) Traffic sign detection and recognition using features combination and random forests. Int J Adv Comput Sci Appl 7(1):686–693

    Google Scholar 

  • Ellahyani Ayoub, El Ansari Mohamed, Lahmyed Redouan, Trémeau Alain (2018) Traffic sign recognition method for intelligent vehicles. JOSA A 35(11):1907–1914

    Article  Google Scholar 

  • Fan Yanjun, Zhang Weigong (2015) Traffic sign detection and classification for advanced driver assistant systems. In: 2015 12th international conference on Fuzzy systems and knowledge discovery (FSKD), pp 1335–1339. IEEE

  • Freund Yoav, Schapire Robert, Abe N (1999) A short introduction to boosting. J Jpn Soc Art Intell 14(771–780):1612

    Google Scholar 

  • Gavrila Dariu M (1998) Multi-feature hierarchical template matching using distance transforms. In: Proceedings. Fourteenth international conference on pattern recognition (Cat. No. 98EX170), vol 1, IEEE, pp 439–444

  • Gómez-Moreno Hilario, Maldonado-Bascón Saturnino, Gil-Jiménez Pedro, Lafuente-Arroyo Sergio (2010) Goal evaluation of segmentation algorithms for traffic sign recognition. IEEE Trans Intell Transp Syst 11(4):917–930

    Article  Google Scholar 

  • Greenhalgh Jack, Mirmehdi Majid (2012) Real-time detection and recognition of road traffic signs. IEEE Trans Intell Transp Syst 13(4):1498–1506

    Article  Google Scholar 

  • Houben Sebastian (2011) A single target voting scheme for traffic sign detection. In: 2011 IEEE intelligent vehicles symposium (IV), pp 124–129. IEEE

  • Islam Kh Tohidul, Raj Ram Gopal (2017) Real-time (vision-based) road sign recognition using an artificial neural network. Sensors 17(4):853

    Article  Google Scholar 

  • Jiang Ning, Jiu Xu, Goto Satoshi (2012) Pedestrian detection using gradient local binary patterns. IEICE Trans Fundam Electron Commun Comput Sci 95(8):1280–1287

    Article  Google Scholar 

  • John Canny (1987) A computational approach to edge detection. Readings in computer vision. Elsevier, Amsterdam, pp 184–203

    Google Scholar 

  • Kerkaou Zakaria, El Ansari Mohamed (2020) Support vector machines based stereo matching method for advanced driver assistance systems. Multimed Tools Appl 79(37):27039–27055

    Article  Google Scholar 

  • Kerkaou Zakaria, Alioua Nawal, El Ansari Mohamed, Masmoudi Lhoussaine (2018) Edge points-based stereo matching approach for omnidirectional images. J Electron Imag 27(5):053015

    Article  Google Scholar 

  • Lahmyed Redouan, El Ansari Mohamed (2016) Multisensors-based pedestrian detection system. In: 2016 IEEE/ACS 13th international conference of computer systems and applications (AICCSA), pp 1–4. IEEE

  • Lahmyed Redouan, El Ansari Mohamed, Tremeau Alain, Kerkaou Zakaria (2018) Camera-light detection and ranging data fusion-based system for pedestrian detection. J Electron Imag 27(6):063011

    Google Scholar 

  • Lahmyed Redouan, El Ansari Mohamed, Ellahyani Ayoub (2019) A new thermal infrared and visible spectrum images-based pedestrian detection system. Multimed Tools Appl 78(12):15861–15885

    Article  Google Scholar 

  • Larsson Fredrik, Felsberg Michael (2011) Using Fourier descriptors and spatial models for traffic sign recognition. In: Scandinavian conference on image analysis. Springer, pp 238–249

  • Lillo-Castellano JM, Mora-Jiménez I, Figuera-Pozuelo Carlos, Rojo-Álvarez José Luis (2015) Traffic sign segmentation and classification using statistical learning methods. Neurocomputing 153:286–299

    Article  Google Scholar 

  • Liu Huaping, Liu Yulong, Sun Fuchun (2014) Traffic sign recognition using group sparse coding. Inf Sci 266:75–89

    Article  Google Scholar 

  • Liu Yifeng, Zeng Lin, Huang Yan (2014) An efficient hog-albp feature for pedestrian detection. SIViP 8(1):125–134

    Article  Google Scholar 

  • Liu Yi, Ling Jie, Qianhong Wu, Qin Bo (2016) Scalable privacy-enhanced traffic monitoring in vehicular ad hoc networks. Soft Comput 20(8):3335–3346

    Article  Google Scholar 

  • Madadlou Ashkan, Emam-Djomeh Zahra, Mousavi Mohamad Ebrahimzadeh, Ehsani Mohamadreza, Javanmard Majid, Sheehan David (2009) Response surface optimization of an artificial neural network for predicting the size of re-assembled casein micelles. Comput Electron Agric 68(2):216–221

    Article  Google Scholar 

  • Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96:226–231

    Google Scholar 

  • Miura Jun, Kanda Tsuyoshi, Shirai Yoshiaki (2000) An active vision system for real-time traffic sign recognition. In: ITSC2000. 2000 IEEE intelligent transportation systems. Proceedings (Cat. No. 00TH8493). IEEE, pp 52–57

  • Mogelmose Andreas, Trivedi Mohan Manubhai, Moeslund Thomas B (2012) Vision-based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans Intell Transp Syst 13(4):1484–1497

    Article  Google Scholar 

  • Mohamed El Ansari, Redouan Lahmyed, Alain Trémeau (2018) A hybrid pedestrian detection system based on visible images and lidar data. VISIGRAPP (5: VISAPP) 2018:325–334

    Google Scholar 

  • Ojala Timo, Pietikäinen Matti, Harwood David (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–59

    Article  Google Scholar 

  • Preethaa KR Sri, Sabari A (2020) Intelligent video analysis for enhanced pedestrian detection by hybrid metaheuristic approach. Soft Comput, 1–9

  • Ruta Andrzej, Li Yongmin, Liu Xiaohui (2010) Real-time traffic sign recognition from video by class-specific discriminative features. Pattern Recogn 43(1):416–430

    Article  Google Scholar 

  • Saha Sanjit Kumar, Chakraborty Dulal, Bhuiyan Md Al-Amin (2012) Neural network based road sign recognition. Int J Comput Appl, 50(10)

  • Salti Samuele, Petrelli Alioscia, Tombari Federico, Fioraio Nicola, Di Stefano Luigi (2015) Traffic sign detection via interest region extraction. Pattern Recogn 48(4):1039–1049

    Article  Google Scholar 

  • Sermanet Pierre, LeCun Yann (2011) Traffic sign recognition with multi-scale convolutional networks. In: The 2011 international joint conference on neural networks. IEEE, pp 2809–2813

  • Shechtman Eli, Irani Michal (2007) Matching local self-similarities across images and videos. In: Computer vision and pattern recognition, 2007. CVPR’07. IEEE Conference on. IEEE, pp 1–8

  • Stallkamp Johannes, Schlipsing Marc, Salmen Jan, Igel Christian (2011) The German traffic sign recognition benchmark: a multi-class classification competition. In: The 2011 international joint conference on neural networks. IEEE, pp 1453–1460

  • Stallkamp Johannes, Schlipsing Marc, Salmen Jan, Igel Christian (2012) Man versus computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Netw 32:323–332

    Article  Google Scholar 

  • Sudha D, Priyadarshini J (2020) An intelligent multiple vehicle detection and tracking using modified vibe algorithm and deep learning algorithm. Soft Comput 24:17417–17429

    Article  Google Scholar 

  • Sugata Banerji, Abhishek Verma, Chengjun Liu (2012) Lbp and color descriptors for image classification. Cross disciplinary biometric systems. Springer, Heidelberg, pp 205–225

    Google Scholar 

  • Sun Zhan-Li, Wang Han, Lau Wai-Shing, Seet Gerald, Wang Danwei (2014) Application of bw-elm model on traffic sign recognition. Neurocomputing 128:153–159

    Article  Google Scholar 

  • Trefnỳ Jirí, Matas Jirí (2010) Extended set of local binary patterns for rapid object detection. In: Computer vision winter workshop, pp 1–7

  • Vapnik Vladimir Naumovich, Vapnik Vlamimir (1998) Statistical learning theory, vol 1. Wiley, New York

    MATH  Google Scholar 

  • Yang Chuan, Zhang Lihe, Huchuan Lu, Ruan Xiang, Yang Ming-Hsuan (2013) Saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3166–3173

  • Yang Jiaolong, Liang Wei, Jia Yunde (2012) Face pose estimation with combined 2d and 3d hog features. In: Pattern Recognition (ICPR), 2012 21st international conference on. IEEE, pages 2492–2495

  • Yang Xing (2013) Enhancement for road sign images and its performance evaluation. Optik 124(14):1957–1960

  • Yuan Xue, Guo Jiaqi, Hao Xiaoli, Chen Houjin (2015) Traffic sign detection via graph-based ranking and segmentation algorithms. IEEE Trans Syst Man Cybern Syst 45(12):1509–1521

    Article  Google Scholar 

  • Zaklouta Fatin, Stanciulescu Bogdan (2011) Warning traffic sign recognition using a hog-based kd tree. In: 2011 IEEE intelligent vehicles symposium (IV), pp 1019–1024. IEEE

  • Zaklouta Fatin, Stanciulescu Bogdan (2012) Real-time traffic-sign recognition using tree classifiers. IEEE Trans Intell Transp Syst 13(4):1507–1514

    Article  Google Scholar 

  • Zhu Zhe, Liang Dun, Zhang Songhai, Huang Xiaolei, Li Baoli, Shimin Hu (2016) Traffic-sign detection and classification in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2110–2118

Download references

Funding

This research work was supported by the National Center for Scientific and technical Research (CNRST), research grant No: 20UIZ2015.

Author information

Authors and Affiliations

  1. LabSIV, Department of Computer Science, Faculty of Science, Ibn Zohr University, BP 8106, 80000, Agadir, Morocco

    Redouan Lahmyed, Mohamed El Ansari & Zakaria Kerkaou

  2. Informatics and Applications Laboratory, Department of Computer Science, Faculty of Science, My Ismail University, Meknès, Morocco

    Mohamed El Ansari

Authors
  1. Redouan Lahmyed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed El Ansari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zakaria Kerkaou
    View author publications

    You can also search for this author in PubMed Google Scholar

Ethics declarations

Conflict of Interest

All the authors have no conflict of interest to declare.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This research work was supported by the National Center for Scientific and Technical Research (CNRST), Research Grant No: 20UIZ2015.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lahmyed, R., Ansari, M.E. & Kerkaou, Z. Automatic road sign detection and recognition based on neural network. Soft Comput 26, 1743–1764 (2022). https://doi.org/10.1007/s00500-021-06726-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-06726-w

Keywords

Search

Navigation