Abstract
Automation of road sign detection and recognition is an important task in the context of applications like self-driving cars. Current popular, state of the art detectors, employing Deep Neural Networks (DNN) are very accurate but at the same time, very complex and have a very high processing time which might not be desirable for real-time applications in autonomous vehicles. In this paper, we present a road sign detection cum recognition pipeline, which exhibits the potential to achieve a considerable speed-up over the DNN based detection algorithms with a relatively small reduction in accuracy. The purpose of the detector is to capture as many road signs as possible in the least possible time. We also propose several techniques at the recognition stage to improve the performance of the pipeline. Comparison has been made to various state-of-the-art DNN based detector pipelines. The proposed pipeline is the fastest amongst all the detection-cum-recognition pipelines with an average processing time of 0.103 secs. per frame. The best F-score achieved by the pipeline is 0.87377. In comparison to this Faster R-CNN achieved the best F-Score of 0.9474 but with an average processing time of 17.664 secs. per frame.
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References
Alghmgham, D.A., Latif, G., Alghazo, J., Alzubaidi, L.: Autonomous traffic sign (ATSR) detection and recognition using deep CNN. Procedia Comput. Sci. 163, 266–274 (2019)
Arcos-Garcia, A., Alvarez-Garcia, J.A., Soria-Morillo, L.M.: Evaluation of deep neural networks for traffic sign detection systems. Neurocomputing 316, 332–344 (2018)
CireşAn, D., Meier, U., Masci, J., Schmidhuber, J.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333–338 (2012)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE (2005)
Dollár, P.: Piotr’s computer vision matlab toolbox (PMT) (2014)
Dollár, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532–1545 (2014)
Ellahyani, A., El Ansari, M., El Jaafari, I.: Traffic sign detection and recognition based on random forests. Appl. Soft Comput. 46, 805–815 (2016)
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Greenhalgh, J., Mirmehdi, M.: Real-time detection and recognition of road traffic signs. IEEE Trans. Intell. Transp. Syst. 13(4), 1498–1506 (2012)
Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., Igel, C.: Detection of traffic signs in real-world images: the German traffic sign detection benchmark. In: International Joint Conference On Neural Networks (2013)
Huang, J., et al.: Speed/accuracy trade-offs for modern convolutional object detectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7310–7311 (2017)
Joseph, R.: Darknet: open source neural networks in C. Pjreddie.com (2016)
Jurišić, F., Filković, I., Kalafatić, Z.: Multiple-dataset traffic sign classification with OneCNN. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 614–618. IEEE (2015)
Liang, M., Yuan, M., Hu, X., Li, J., Liu, H.: Traffic sign detection by ROI extraction and histogram features-based recognition. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2013)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Liu, C., Chang, F., Chen, Z.: Rapid multiclass traffic sign detection in high-resolution images. IEEE Trans. Intell. Transp. Syst. 15(6), 2394–2403 (2014)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Mathias, M., Timofte, R., Benenson, R., Van Gool, L.: Traffic sign recognition–how far are we from the solution? In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2013)
Møgelmose, A., Liu, D., Trivedi, M.M.: Detection of us traffic signs. IEEE Trans. Intell. Transp. Syst. 16(6), 3116–3125 (2015)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Sermanet, P., LeCun, Y.: Traffic sign recognition with multi-scale convolutional networks. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2809–2813 (2011)
Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The German traffic sign recognition benchmark: a multi-class classification competition. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 1453–1460 (2011)
Trieu, T.H.: Darkflow. GitHub Repository (2018). https://github.com/thtrieu/darkflow. Accessed 14 Febr 2019
Vennelakanti, A., Shreya, S., Rajendran, R., Sarkar, D., Muddegowda, D., Hanagal, P.: Traffic sign detection and recognition using a CNN ensemble. In: 2019 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–4. IEEE (2019)
Wali, S.B., et al.: Vision-based traffic sign detection and recognition systems: current trends and challenges. Sensors 19(9), 2093 (2019)
Wang, G., Ren, G., Wu, Z., Zhao, Y., Jiang, L.: A robust, coarse-to-fine traffic sign detection method. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–5. IEEE (2013)
Youssef, A., Albani, D., Nardi, D., Bloisi, D.D.: Fast traffic sign recognition using color segmentation and deep convolutional networks. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2016. LNCS, vol. 10016, pp. 205–216. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48680-2_19
Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., Hu, S.: Traffic-sign detection and classification in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2110–2118 (2016)
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Ansari, F.J., Agarwal, S. (2021). Fast Road Sign Detection and Recognition Using Colour-Based Thresholding. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1378. Springer, Singapore. https://doi.org/10.1007/978-981-16-1103-2_27
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