{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:41:36Z","timestamp":1742913696086,"version":"3.40.3"},"publisher-location":"Cham","reference-count":58,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031082764"},{"type":"electronic","value":"9783031082771"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-08277-1_30","type":"book-chapter","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T12:13:01Z","timestamp":1655381581000},"page":"362-376","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Improvement of CNN Model for Traffic Sign Recognition and Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4791-4227","authenticated-orcid":false,"given":"Tahar","family":"Mekhaznia","sequence":"first","affiliation":[]},{"given":"Imtiez","family":"Fares","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"key":"30_CR1","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.cjtee.2021.01.007","volume":"24","author":"JY He","year":"2021","unstructured":"He, J.Y., et al.: Road traffic injury mortality and morbidity by country development status, 2011\u20132017. Chin. J. Traumatol. \u2013 Engl. Ed. 24, 88\u201393 (2021). https:\/\/doi.org\/10.1016\/j.cjtee.2021.01.007","journal-title":"Chin. J. Traumatol. \u2013 Engl. Ed."},{"key":"30_CR2","doi-asserted-by":"publisher","first-page":"2160","DOI":"10.3390\/s16122160","volume":"16","author":"H Vokhidov","year":"2016","unstructured":"Vokhidov, H., Hong, H.G., Kang, J.K., Hoang, T.M., Park, K.R.: Recognition of damaged arrow-road markings by visible light camera sensor based on convolutional neural network. Sensors (Switz.) 16, 2160 (2016). https:\/\/doi.org\/10.3390\/s16122160","journal-title":"Sensors (Switz.)"},{"key":"30_CR3","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1007\/s40313-021-00751-8","volume":"32","author":"LA Reis","year":"2021","unstructured":"Reis, L.A., Pereira, S.L., Dias, E.M., Scoton, M.L.D.: Adaptative optimal control of nonlinear systems simulation to support hazardous materials traffic management. J. Control Autom. Electr. Syst. 32, 1143\u20131152 (2021). https:\/\/doi.org\/10.1007\/s40313-021-00751-8","journal-title":"J. Control Autom. Electr. Syst."},{"key":"30_CR4","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1080\/07347324.2020.1830734","volume":"39","author":"M Scherer","year":"2021","unstructured":"Scherer, M., et al.: Typologies of drivers convicted of driving under the influence of alcohol as predictors of alcohol ignition interlock performance. Alcohol. Treat. Q. 39, 96\u2013109 (2021). https:\/\/doi.org\/10.1080\/07347324.2020.1830734","journal-title":"Alcohol. Treat. Q."},{"key":"30_CR5","doi-asserted-by":"publisher","first-page":"11268","DOI":"10.1109\/ACCESS.2020.2963854","volume":"8","author":"W Yang","year":"2020","unstructured":"Yang, W., Wan, B., Qu, X.: A forward collision warning system using driving intention recognition of the front vehicle and V2V communication. IEEE Access 8, 11268\u201311278 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2963854","journal-title":"IEEE Access"},{"key":"30_CR6","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/978-981-15-5856-6_13","volume-title":"Proceedings of Fifth International Congress on Information and Communication Technology","author":"S Krishnarao","year":"2021","unstructured":"Krishnarao, S., Wang, H.-C., Sharma, A., Iqbal, M.: Enhancement of advanced driver assistance system (ADAS) using machine learning. In: Yang, X.-S., Sherratt, R.S., Dey, N., Joshi, A. (eds.) ICICT 2020. AISC, vol. 1183, pp. 139\u2013146. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-15-5856-6_13"},{"key":"30_CR7","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1109\/MITS.2019.2907630","volume":"11","author":"E Mart\u00ed","year":"2019","unstructured":"Mart\u00ed, E., De Miguel, M.\u00c1., Garc\u00eda, F., P\u00e9rez, J.: A review of sensor technologies for perception in automated driving. IEEE Intell. Transp. Syst. Mag. 11, 94\u2013108 (2019). https:\/\/doi.org\/10.1109\/MITS.2019.2907630","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"30_CR8","doi-asserted-by":"publisher","first-page":"86578","DOI":"10.1109\/ACCESS.2019.2924947","volume":"7","author":"C Liu","year":"2019","unstructured":"Liu, C., Li, S., Chang, F., Wang, Y.: Machine vision based traffic sign detection methods: review, analyses and perspectives. IEEE Access 7, 86578\u201386596 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2924947","journal-title":"IEEE Access"},{"key":"30_CR9","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3389\/frai.2020.00004","volume":"3","author":"F Emmert-Streib","year":"2020","unstructured":"Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., Dehmer, M.: An introductory review of deep learning for prediction models with big data. Front. Artif. Intell. 3, 4 (2020). https:\/\/doi.org\/10.3389\/frai.2020.00004","journal-title":"Front. Artif. Intell."},{"key":"30_CR10","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/h0042519","volume":"65","author":"F Rosenblatt","year":"1958","unstructured":"Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386 (1958). https:\/\/doi.org\/10.1037\/h0042519","journal-title":"Psychol. Rev."},{"key":"30_CR11","doi-asserted-by":"publisher","first-page":"i-216","DOI":"10.2200\/S00787ED1V01Y201707CSL009","volume":"8","author":"S Liu","year":"2018","unstructured":"Liu, S., Li, L., Tang, J., Wu, S., Gaudiot, J.L.: Creating autonomous vehicle systems. Synth. Lect. Comput. Sci. 8, i\u2013216 (2018). https:\/\/doi.org\/10.2200\/S00787ED1V01Y201707CSL009","journal-title":"Synth. Lect. Comput. Sci."},{"key":"30_CR12","first-page":"58","volume":"7","author":"T Surinwarangkoon","year":"2013","unstructured":"Surinwarangkoon, T., Nitsuwat, S., Elvin, J.: A traffic sign detection and recognition system. Int. J. Circuits Syst. Signal Process. 7, 58\u201365 (2013)","journal-title":"Int. J. Circuits Syst. Signal Process."},{"key":"30_CR13","doi-asserted-by":"publisher","first-page":"4021","DOI":"10.3390\/s19184021","volume":"19","author":"J Cao","year":"2019","unstructured":"Cao, J., Song, C., Peng, S., Xiao, F., Song, S.: Improved traffic sign detection and recognition algorithm for intelligent vehicles. Sensors (Switz.) 19, 4021 (2019). https:\/\/doi.org\/10.3390\/s19184021","journal-title":"Sensors (Switz.)"},{"key":"30_CR14","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1016\/j.asoc.2015.12.041","volume":"46","author":"A Ellahyani","year":"2016","unstructured":"Ellahyani, A., El Ansari, M., El Jaafari, I.: Traffic sign detection and recognition based on random forests. Appl. Soft Comput. J. 46, 805\u2013815 (2016). https:\/\/doi.org\/10.1016\/j.asoc.2015.12.041","journal-title":"Appl. Soft Comput. J."},{"key":"30_CR15","doi-asserted-by":"publisher","unstructured":"Wu, X., Wei, Z., Hu, Y., Wang, L.: Traffic sign detection method using multi-color space fusion (2020). https:\/\/doi.org\/10.1109\/ICAICA50127.2020.9182603","DOI":"10.1109\/ICAICA50127.2020.9182603"},{"key":"30_CR16","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.eswa.2015.11.018","volume":"48","author":"S Kaplan Berkaya","year":"2016","unstructured":"Kaplan Berkaya, S., Gunduz, H., Ozsen, O., Akinlar, C., Gunal, S.: On circular traffic sign detection and recognition. Expert Syst. Appl. 48, 67\u201375 (2016). https:\/\/doi.org\/10.1016\/j.eswa.2015.11.018","journal-title":"Expert Syst. Appl."},{"key":"30_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/978-3-319-63309-1_32","volume-title":"Intelligent Computing Theories and Application","author":"H Huang","year":"2017","unstructured":"Huang, H., Hou, L.-Y.: Traffic road sign detection and recognition in natural environment using RGB color model. In: Huang, D.-S., Bevilacqua, V., Premaratne, P., Gupta, P. (eds.) ICIC 2017. LNCS, vol. 10361, pp. 345\u2013352. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-63309-1_32"},{"key":"30_CR18","first-page":"914","volume":"3","author":"C Gubbi","year":"2014","unstructured":"Gubbi, C.: Automatic tracking of traffic signs based on HSV. Int. J. Eng. Res. Technol. 3, 914\u2013917 (2014)","journal-title":"Int. J. Eng. Res. Technol."},{"key":"30_CR19","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/978-981-15-4015-8_8","volume-title":"Computer Vision and Image Processing","author":"B Vaidya","year":"2020","unstructured":"Vaidya, B., Paunwala, C.: Traffic sign recognition using color and spatial transformer network on GPU embedded development board. In: Nain, N., Vipparthi, S.K., Raman, B. (eds.) CVIP 2019. CCIS, vol. 1147, pp. 82\u201393. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-4015-8_8"},{"key":"30_CR20","doi-asserted-by":"publisher","first-page":"600","DOI":"10.25046\/AJ050471","volume":"5","author":"A Santos","year":"2020","unstructured":"Santos, A., Abu, P.A., Oppus, C., Reyes, R.: Real-time traffic sign detection and recognition system for assistive driving. Adv. Sci. Technol. Eng. Syst. 5, 600\u2013611 (2020). https:\/\/doi.org\/10.25046\/AJ050471","journal-title":"Adv. Sci. Technol. Eng. Syst."},{"key":"30_CR21","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.procs.2019.12.108","volume":"163","author":"DA Alghmgham","year":"2019","unstructured":"Alghmgham, D.A., Latif, G., Alghazo, J., Alzubaidi, L.: Autonomous Traffic Sign (ATSR) Detection and Recognition using Deep CNN. Proc. Comput. Sci. 163, 266\u2013274 (2019). https:\/\/doi.org\/10.1016\/j.procs.2019.12.108","journal-title":"Proc. Comput. Sci."},{"key":"30_CR22","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1109\/TITS.2019.2913588","volume":"21","author":"D Tabernik","year":"2020","unstructured":"Tabernik, D., Skocaj, D.: Deep learning for large-scale traffic-sign detection and recognition. IEEE Trans. Intell. Transp. Syst. 21, 1427\u20131440 (2020). https:\/\/doi.org\/10.1109\/TITS.2019.2913588","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"30_CR23","doi-asserted-by":"publisher","unstructured":"Sun, Y., Ge, P., Liu, D.: Traffic sign detection and recognition based on convolutional neural network (2019). https:\/\/doi.org\/10.1109\/CAC48633.2019.8997240","DOI":"10.1109\/CAC48633.2019.8997240"},{"key":"30_CR24","doi-asserted-by":"publisher","unstructured":"Alhabshee, S.M., Bin Shamsudin, A.U.: Deep learning traffic sign recognition in autonomous vehicle (2020). https:\/\/doi.org\/10.1109\/SCOReD50371.2020.9251034","DOI":"10.1109\/SCOReD50371.2020.9251034"},{"key":"30_CR25","doi-asserted-by":"publisher","unstructured":"Abdi, L., Meddeb, A.: Deep learning traffic sign detection, recognition and augmentation (2017). https:\/\/doi.org\/10.1145\/3019612.3019643","DOI":"10.1145\/3019612.3019643"},{"key":"30_CR26","doi-asserted-by":"publisher","first-page":"6997","DOI":"10.3390\/app10196997","volume":"10","author":"SK Tai","year":"2020","unstructured":"Tai, S.K., Dewi, C., Chen, R.C., Liu, Y.T., Jiang, X., Yu, H.: Deep learning for traffic sign recognition based on spatial pyramid pooling with scale analysis. Appl. Sci. 10, 6997 (2020). https:\/\/doi.org\/10.3390\/app10196997","journal-title":"Appl. Sci."},{"key":"30_CR27","doi-asserted-by":"publisher","unstructured":"Li, D., Zhao, D., Chen, Y., Zhang, Q.: DeepSign: deep learning based traffic sign recognition (2018). https:\/\/doi.org\/10.1109\/IJCNN.2018.8489623","DOI":"10.1109\/IJCNN.2018.8489623"},{"key":"30_CR28","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.neunet.2018.01.005","volume":"99","author":"\u00c1 Arcos-Garc\u00eda","year":"2018","unstructured":"Arcos-Garc\u00eda, \u00c1., \u00c1lvarez-Garc\u00eda, J.A., Soria-Morillo, L.M.: Deep neural network for traffic sign recognition systems: an analysis of spatial transformers and stochastic optimisation methods. Neural Netw. 99, 158\u2013165 (2018). https:\/\/doi.org\/10.1016\/j.neunet.2018.01.005","journal-title":"Neural Netw."},{"key":"30_CR29","doi-asserted-by":"publisher","first-page":"138","DOI":"10.3390\/sym9080138","volume":"9","author":"KT Islam","year":"2017","unstructured":"Islam, K.T., Raj, R.G., Mujtaba, G.: Recognition of traffic sign based on bag-of-words and artificial neural network. Symmetry (Basel) 9, 138 (2017). https:\/\/doi.org\/10.3390\/sym9080138","journal-title":"Symmetry (Basel)"},{"key":"30_CR30","doi-asserted-by":"publisher","DOI":"10.12988\/ces.2017.7327","author":"D-C Park","year":"2017","unstructured":"Park, D.-C.: Classification of traffic signs using artificial neural networks. Contemp. Eng. Sci. (2017). https:\/\/doi.org\/10.12988\/ces.2017.7327","journal-title":"Contemp. Eng. Sci."},{"key":"30_CR31","doi-asserted-by":"publisher","first-page":"189855","DOI":"10.1109\/ACCESS.2020.3031191","volume":"8","author":"A Avramovi\u0107","year":"2020","unstructured":"Avramovi\u0107, A., Sluga, D., Tabernik, D., Sko\u010daj, D., Stojni\u0107, V., Ilc, N.: Neural-network-based traffic sign detection and recognition in high-definition images using region focusing and parallelization. IEEE Access 8, 189855\u2013189868 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3031191","journal-title":"IEEE Access"},{"key":"30_CR32","doi-asserted-by":"publisher","first-page":"105","DOI":"10.18287\/2412-6179-2018-42-1-105-112","volume":"42","author":"VI Shakhuro","year":"2018","unstructured":"Shakhuro, V.I., Konushin, A.S.: Image synthesis with neural networks for traffic sign classification. Comput. Opt. 42, 105\u2013112 (2018). https:\/\/doi.org\/10.18287\/2412-6179-2018-42-1-105-112","journal-title":"Comput. Opt."},{"key":"30_CR33","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.neunet.2012.02.016","volume":"32","author":"J Stallkamp","year":"2012","unstructured":"Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Netw. 32, 323\u2013332 (2012). https:\/\/doi.org\/10.1016\/j.neunet.2012.02.016","journal-title":"Neural Netw."},{"key":"30_CR34","doi-asserted-by":"publisher","unstructured":"Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., Hu, S.: Traffic-sign detection and classification in the wild. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016, pp. 2110\u20132118 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.232","DOI":"10.1109\/CVPR.2016.232"},{"key":"30_CR35","doi-asserted-by":"publisher","first-page":"78136","DOI":"10.1109\/ACCESS.2018.2884826","volume":"6","author":"C Gamez Serna","year":"2018","unstructured":"Gamez Serna, C., Ruichek, Y.: Classification of traffic signs: the European dataset. IEEE Access 6, 78136\u201378148 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2884826","journal-title":"IEEE Access"},{"key":"30_CR36","doi-asserted-by":"publisher","unstructured":"Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., Igel, C.: Detection of traffic signs in real-world images: the German traffic sign detection benchmark (2013). https:\/\/doi.org\/10.1109\/IJCNN.2013.6706807","DOI":"10.1109\/IJCNN.2013.6706807"},{"key":"30_CR37","doi-asserted-by":"publisher","first-page":"294","DOI":"10.18287\/2412-6179-2016-40-2-294-300","volume":"40","author":"VI Shakhuro","year":"2016","unstructured":"Shakhuro, V.I., Konushin, A.S.: Russian traffic sign images dataset. Comput. Opt. 40, 294\u2013300 (2016). https:\/\/doi.org\/10.18287\/2412-6179-2016-40-2-294-300","journal-title":"Comput. Opt."},{"key":"30_CR38","unstructured":"Rituparna, S.: ITSD (2018). https:\/\/www.mapsofindia.com\/my-india\/government\/traffic-signs-and-road-safety"},{"key":"30_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1007\/978-3-642-21227-7_23","volume-title":"Image Analysis","author":"F Larsson","year":"2011","unstructured":"Larsson, F., Felsberg, M.: Using fourier descriptors and spatial models for traffic sign recognition. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 238\u2013249. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21227-7_23"},{"key":"30_CR40","unstructured":"Brki\u0107, K., Pinz, A., \u0160egvi\u0107, S.: Traffic sign detection as a component of an automated traffic infrastructure inventory system. In: 33rd Annual Workshop of the Austrian Association for Pattern Recognition (2009)"},{"key":"30_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1007\/978-3-030-58592-1_5","volume-title":"Computer Vision \u2013 ECCV 2020","author":"C Ertler","year":"2020","unstructured":"Ertler, C., Mislej, J., Ollmann, T., Porzi, L., Neuhold, G., Kuang, Y.: The mapillary traffic sign dataset for detection and classification on a global scale. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 68\u201384. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58592-1_5"},{"key":"30_CR42","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1134\/S1054661818010182","volume":"28","author":"A Staravoitau","year":"2018","unstructured":"Staravoitau, A.: Traffic sign classification with a convolutional network. Pattern Recognit. Image Anal. 28, 155\u2013162 (2018). https:\/\/doi.org\/10.1134\/S1054661818010182","journal-title":"Pattern Recognit. Image Anal."},{"key":"30_CR43","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1016\/j.proeng.2017.09.594","volume":"201","author":"A Shustanov","year":"2017","unstructured":"Shustanov, A., Yakimov, P.: CNN design for real-time traffic sign recognition. Proc. Eng. 201, 718\u2013725 (2017). https:\/\/doi.org\/10.1016\/j.proeng.2017.09.594","journal-title":"Proc. Eng."},{"key":"30_CR44","doi-asserted-by":"publisher","unstructured":"Burleigh, N., King, J., Braunl, T.: Deep learning for autonomous driving (2019). https:\/\/doi.org\/10.1109\/DICTA47822.2019.8945818","DOI":"10.1109\/DICTA47822.2019.8945818"},{"key":"30_CR45","doi-asserted-by":"publisher","first-page":"429","DOI":"10.20537\/2076-7633-2021-13-2-429-435","volume":"13","author":"AI Sabirov","year":"2021","unstructured":"Sabirov, A.I., Katasev, A.S., Dagaeva, M.V.: A neural network model for traffic signs recognition in intelligent transport systems. Comput. Res. Model. 13, 429\u2013435 (2021). https:\/\/doi.org\/10.20537\/2076-7633-2021-13-2-429-435","journal-title":"Comput. Res. Model."},{"issue":"1","key":"30_CR46","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s13177-019-00178-1","volume":"18","author":"A Alam","year":"2019","unstructured":"Alam, A., Jaffery, Z.A.: Indian traffic sign detection and recognition. Int. J. Intell. Transp. Syst. Res. 18(1), 98\u2013112 (2019). https:\/\/doi.org\/10.1007\/s13177-019-00178-1","journal-title":"Int. J. Intell. Transp. Syst. Res."},{"key":"30_CR47","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1515\/phys-2018-0135","volume":"16","author":"S Guo","year":"2018","unstructured":"Guo, S., Yang, X.: Fast recognition algorithm for static traffic sign information. Open Phys. 16, 1149\u20131156 (2018). https:\/\/doi.org\/10.1515\/phys-2018-0135","journal-title":"Open Phys."},{"key":"30_CR48","doi-asserted-by":"publisher","first-page":"418","DOI":"10.17586\/2226-1494-2020-20-3-418-424","volume":"127","author":"VN Sichkar","year":"2020","unstructured":"Sichkar, V.N., Kolyubin, S.A.: Real time detection and classification of traffic signs based on YOLO Version 3 algorithm. Sci. Tech. J. Inf. Technol. Mech. Opt. 127, 418\u2013424 (2020). https:\/\/doi.org\/10.17586\/2226-1494-2020-20-3-418-424","journal-title":"Sci. Tech. J. Inf. Technol. Mech. Opt."},{"key":"30_CR49","doi-asserted-by":"publisher","first-page":"97228","DOI":"10.1109\/ACCESS.2021.3094201","volume":"9","author":"C Dewi","year":"2021","unstructured":"Dewi, C., Chen, R.C., Liu, Y.T., Jiang, X., Hartomo, K.D.: Yolo V4 for advanced traffic sign recognition with synthetic training data generated by various GAN. IEEE Access 9, 97228\u201397242 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3094201","journal-title":"IEEE Access"},{"key":"30_CR50","unstructured":"Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization (2015)"},{"key":"30_CR51","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/978-3-030-70866-5_17","volume-title":"Machine Learning for Networking","author":"M Kherarba","year":"2021","unstructured":"Kherarba, M., Abbes, M.T., Boumerdassi, S., Meddah, M., Benhamada, A., Senouci, M.: Road sign identification with convolutional neural network using TensorFlow. In: Renault, \u00c9., Boumerdassi, S., M\u00fchlethaler, P. (eds.) MLN 2020. LNCS, vol. 12629, pp. 255\u2013264. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-70866-5_17"},{"key":"30_CR52","doi-asserted-by":"publisher","unstructured":"Narejo, S., Talpur, S., Memon, M., Rahoo, A.: An automated system for traffic sign recognition using convolutional neural network. 3C Tecnol. innovaci\u00f3n Apl. a la pyme 9, 119\u2013135 (2020). https:\/\/doi.org\/10.17993\/3ctecno.2020.specialissue6.119-135","DOI":"10.17993\/3ctecno.2020.specialissue6.119-135"},{"key":"30_CR53","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8870529","author":"A Zaibi","year":"2021","unstructured":"Zaibi, A., Ladgham, A., Sakly, A.: A lightweight model for traffic sign classification based on enhanced LeNet-5 network. J. Sens. (2021). https:\/\/doi.org\/10.1155\/2021\/8870529","journal-title":"J. Sens."},{"key":"30_CR54","doi-asserted-by":"publisher","first-page":"4244","DOI":"10.17762\/turcomat.v12i3.1715","volume":"12","author":"A Velamati","year":"2021","unstructured":"Velamati, A., et al.: Traffic sign classification using convolutional neural networks and computer vision. Turkish J. Comput. Math. Educ. 12, 4244\u20134250 (2021). https:\/\/doi.org\/10.17762\/turcomat.v12i3.1715","journal-title":"Turkish J. Comput. Math. Educ."},{"key":"30_CR55","doi-asserted-by":"publisher","first-page":"853","DOI":"10.3390\/s17040853","volume":"17","author":"KT Islam","year":"2017","unstructured":"Islam, K.T., Raj, R.G.: Real-time (vision-based) road sign recognition using an artificial neural network. Sensors (Switz.) 17, 853 (2017). https:\/\/doi.org\/10.3390\/s17040853","journal-title":"Sensors (Switz.)"},{"key":"30_CR56","doi-asserted-by":"publisher","unstructured":"Wen, L., Jo, K.H.: Traffic sign recognition and classification with modified residual networks (2018). https:\/\/doi.org\/10.1109\/SII.2017.8279326","DOI":"10.1109\/SII.2017.8279326"},{"key":"30_CR57","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3645805","author":"T Chaudhari","year":"2020","unstructured":"Chaudhari, T., Wale, A., Joshi, A., Sawant, S.: Traffic sign recognition using small-scale convolutional neural network. SSRN Electron. J. (2020). https:\/\/doi.org\/10.2139\/ssrn.3645805","journal-title":"SSRN Electron. J."},{"key":"30_CR58","doi-asserted-by":"publisher","unstructured":"Sermanet, P., Lecun, Y.: Traffic sign recognition with multi-scale convolutional networks (2011). https:\/\/doi.org\/10.1109\/IJCNN.2011.6033589","DOI":"10.1109\/IJCNN.2011.6033589"}],"container-title":["Communications in Computer and Information Science","Intelligent Systems and Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-08277-1_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T12:19:09Z","timestamp":1655381949000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08277-1_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031082764","9783031082771"],"references-count":58,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08277-1_30","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems and Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hammamet","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tunisia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 March 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 March 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ispr22022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ispr2022.sciencesconf.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}