一种圆形禁令交通标志快速提取和识别方法
A Quickly Detecting and Recognition Method for Circular Ban Traffic Signs

作者: 陈力 , 李迎松 :武汉大学遥感信息工程学院,湖北 武汉;

关键词: 交通标志识别与检测BP人工神经网络颜色分割形状检测Traffic Signs Detecting and Recognition BP Artificial Neural Network Color Segmentation Shape Analysis

摘要: 随着社会的发展,智能交通系统的开发受到广泛的关注,交通标志识别系统作为智能交通的一部分,如何对其快速的提取并识别开始被越来越多的人关注和研究。本文提出一种基于RGB空间的圆形禁令交通标志的提取与识别算法,该方法根据标志的颜色特征和形状特征,通过RGB空间阈值分割和最小二乘椭圆拟合过滤的方法对交通标志进行检测,最后利用BP人工神经网络构建最佳识别网络,达到自动识别圆形禁令交通标志的目的。实验结果表明,该方法具有较好的提取和识别能力。

Abstract: With the development of the society, the intelligent transportation system has been widely focused on. More and more people are paying attention to traffic sign recognition system, which as a part of the entire intelligent transportation system. This paper put forward a method, which based on RGB space, to detect and to recognize circular ban traffic signs. This method uses threshold segmentation in RGB space and the least-squares ellipse fitting to filter and detect traffic signs, due to their color features and shape features. At last, the method uses BP artificial neural network to build the best recognition network to automatically recognize circular ban traffic signs. The experimental results show that the method has good detect and recognize ability.

文章引用: 陈力 , 李迎松 (2016) 一种圆形禁令交通标志快速提取和识别方法。 测绘科学技术, 4, 45-52. doi: 10.12677/GST.2016.42006

参考文献

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http://dx.doi.org/10.1109/TVT.2002.1002505

[2] De La Escalera, A., Moreno, L.E., Salichs, M.A., et al. (1997) Road Traffic Sign Detection and Classification. IEEE Transactions on Industrial Electronics, 44, 848-859.
http://dx.doi.org/10.1109/41.649946

[3] Asakura, T., Aoyagi, Y. and Hirose, O.K. (2000) Real-Time Recognition of Road Traffic Sign in Moving Scene Image Using New Image Filter. Proceeding s of the 39th SICE Annual Conference, SICE, Japan, 13-18.

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http://dx.doi.org/10.1109/TVT.2003.810999

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[15] 黄志勇, 孙光民, 李芳. 基于RGB视觉模型的交通标志分割[J]. 微电子与计算机技术, 2004, 21(10): 147-152.

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[19] Perez, E. and Javidi, B. (2002) Nonlinear Dist Ort Ion-Tolerant Filters for Detection of Road Signs in Background Noise. IEEE Transactions on Vehicular Technology, 51, 567-576.
http://dx.doi.org/10.1109/TVT.2002.1002505

[20] De La Escalera, A., Moreno, L.E., Salichs, M.A., et al. (1997) Road Traffic Sign Detection and Classification. IEEE Transactions on Industrial Electronics, 44, 848-859.
http://dx.doi.org/10.1109/41.649946

[21] Asakura, T., Aoyagi, Y. and Hirose, O.K. (2000) Real-Time Recognition of Road Traffic Sign in Moving Scene Image Using New Image Filter. Proceeding s of the 39th SICE Annual Conference, SICE, Japan, 13-18.

[22] Fang, C.Y., Chen, S.W. and Fuh, C.S. (2003) Road-Sign Detection and Tracking. IEEE Transactions on Vehicular Technology, 52, 1329-1341.
http://dx.doi.org/10.1109/TVT.2003.810999

[23] 朱双东, 张懿, 陆晓峰. 三角形交通标志的智能检测方法[J]. 中国图象图形学报, 2006, 11(8): 1127-1131.

[24] Yves, L.B., Denis, G. and Frank, F.P. (2001) A Model-Based Road Sign Identification System. IEEE Computer Society, 1163.

[25] La Escalera and Miguel, S.L. (1996) Road Traffic Sign Detection and Classification. IEEE Transactions on Industrial Electronics, 848-859.

[26] Ghics, D. and Yuan, X.B. (1995) Recognition of Traffic Signs by Artificial Neural Network. IEEE Conference on Neural Network, 1444-1449.

[27] Vitabile, S., Pollaccia, G. and Pialato, G. (2001) Road Signs Recognition Using a Dynamic Pixel Aggregation Technique. The HSV Color Space, Conference on Image Analysis and Processing, 572-577.

[28] Aoyagi, Y. and Asakura, T. (1996) A Study on Traffic Sign Recognition in Scene Image Using Genetic Algorithms and Neural Networks. Conference on Industrial Electronics, 1838-1843.
http://dx.doi.org/10.1109/iecon.1996.570749

[29] de La Escalera, A. and Armingol, J.M. (2001) Traffic Sign Detection for Driver Support Systems. International Conference on Field and Service Robotics, Helsinki, 11-13 June 2001.

[30] 陈维馨. 道路交通标志检测技术研究[D]: [硕士学位论文]. 厦门: 厦门大学, 2007.

[31] Liu, H., Liu, D. and Xin, J. (2002) Real-Time Recognition of Road Traffic Sign in Motion Image Based on Genetic Algorithm. Proceedings of the International Conference on Machine Learning and Cybernetics, 1, 83-86.
http://dx.doi.org/10.1109/ICMLC.2002.1176714

[32] Maldonado-Bascon, S., Lafuente-Arroyo, S., Gil-Jimenez, P., Gomez-Moreno, H. and Lopez-Ferreras, F. (2007) Road-Sign Detection and Recognition Based on Support Vector Machines. IEEE Transactions on Intelligent Transportation Systems, 8, 264-278.
http://dx.doi.org/10.1109/TITS.2007.895311

[33] 黄志勇, 孙光民, 李芳. 基于RGB视觉模型的交通标志分割[J]. 微电子与计算机技术, 2004, 21(10): 147-152.

[34] Vicen-Bueno, R., Gil-Pita, R., Jarabo-Amores, M.P. and López-Ferreras, F. (2005) Complexity Reduction in Neural Networks Applied to Traffic Sign Recognition Tasks. Pro-ceedings of the 13th European Signal Processing Conference, Antalya, 4-8 September 2005, 1-4.

[35] Kumar, S. (2005) Neural Networks: A Classroom Approach. McGraw-Hill, New York.

[36] Schwarzer, G., Vach, W. and Schumacher, M. (2000) On the Misuses of Artificial Neural Networks for Prognostic and Diagnostic Classification in Oncology. Statistics in Medicine, 19, 541-561.
http://dx.doi.org/10.1002/(SICI)1097-0258(20000229)19:4<541::AID-SIM355>3.0.CO;2-V

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