Abstract
Traffic lights detection is an important task for intelligent vehicles. It is non-trivial due to variance backgrounds and illumination conditions. Therefore, a traffic lights detection system that can apply to different scenes is necessary. In this paper, we research the traffic lights detection based on deep learning, which can extract features with representation and robustness from input image automatically and avoid using artificial features. The approach of traffic lights detection proposed in this paper includes two stages: (1) region proposal and (2) classification of traffic lights. Firstly, we propose a region proposal method based on intensity, color, and geometric information of traffic lights. Secondly, convolutional neural network (CNN) was introduced for the traffic lights classification, obtaining 99.6% average accuracy. For detection, we evaluate our system on 6804 images of different scenes, the recall and accuracy of detection achieve 99.2% and 98.5% respectively.
Sponsored by the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University, zz2019140 and National College Students’ innovation and entrepreneurship training program, 201810699167.
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References
Chen, Z., Yang, J., Kong, B.: A robust traffic sign recognition system for intelligent vehicles. In: 2011 Sixth International Conference on Image and Graphics, pp. 975–980, August 2011
de Charette, R., Nashashibi, F.: Real time visual traffic lights recognition based on spot light detection and adaptive traffic lights templates. In: 2009 IEEE Intelligent Vehicles Symposium, pp. 358–363, June 2009
Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2155–2162, June 2014. https://doi.org/10.1109/CVPR.2014.276
Eric Hague, G.: Color segmentation in the HSI color space using the k-means algorithm. In: Proceedings of the SPIE - The International Society for Optical Engineering, vol. 3026 (1997)
de la Escalera, A., Armingol, J., Mata, M.: Traffic sign recognition and analysis for intelligent vehicles. Image Vis. Comput. 21, 247–258 (2003)
Espinosa, J.E., Velastin, S.A., Branch, J.W.: Vehicle detection using alex net and faster R-CNN deep learning models: a comparative study. In: Badioze Zaman, H., et al. (eds.) IVIC 2017. LNCS, vol. 10645, pp. 3–15. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70010-6_1
Girshick, R.: Fast R-CNN. arXiv e-prints arXiv:1504.08083, April 2015
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv e-prints arXiv:1311.2524 (Nov 2013)
Horak, R., Mattioli, J.: Target detection of very dim objects using gray-level morphologic tophat (1996)
Hosang, J., Benenson, R., Dollár, P., Schiele, B.: What makes for effective detection proposals? IEEE Trans. Pattern Anal. Mach. Intell. 38(4), 814–830 (2016)
Hosang, J., Benenson, R., Schiele, B.: How good are detection proposals, really? (2014)
Jensen, M.B., Philipsen, M.P., Møgelmose, A., Moeslund, T.B., Trivedi, M.M.: Vision for looking at traffic lights: issues, survey, and perspectives. IEEE Trans. Intell. Transp. Syst. 17(7), 1800–1815 (2016)
John, V., Yoneda, K., Liu, Z., Mita, S.: Saliency map generation by the convolutional neural network for real-time traffic light detection using template matching. IEEE Trans. Comput. Imaging 1(3), 159–173 (2015)
John, V., Yoneda, K., Qi, B., Liu, Z., Mita, S.: Traffic light recognition in varying illumination using deep learning and saliency map. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 2286–2291, October 2014
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Leng, B., Yu, K., Liu, Y., Jingyan, Q.: Data augmentation for unbalanced face recognition training sets. Neurocomputing 235, 10–14 (2016)
Li, Y., Cai, Z.X., Gu, M.Q., Yan, Q.Y.: Traffic lights recognition based on morphology filtering and statistical classification, vol. 3, pp. 1700–1704 (2011)
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
Mas Montserrat, D., Lin, Q., Allebach, J., Delp, E.: Training object detection and recognition CNN models using data augmentation. Electron. Imaging 2017, 27–36 (2017)
Omachi, M., Omachi, S.: Traffic light detection with color and edge information, pp. 284–287, January 2009
Ouyang, W., Wang, X.: Joint deep learning for pedestrian detection. In: 2013 IEEE International Conference on Computer Vision, pp. 2056–2063, December 2013
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You Only Look Once: Unified, Real-Time Object Detection. arXiv e-prints arXiv:1506.02640, June 2015
Rotaru, C., Graf, T., Zhang, J.: Color image segmentation in HSI space for automotive applications. J. Real-Time Image Process. 3(4), 311–322 (2008)
Shi, Z., Zou, Z., Zhang, C.: Real-time traffic light detection with adaptive background suppression filter. IEEE Trans. Intell. Transp. Syst. 17(3), 690–700 (2016)
Soille, P.: Morphological Image Analysis-Principles and Applications, vol. 49. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-662-05088-0
Tran, T.H., Pham, C.C., Nguyen, T.P., Duong, T.T., Jeon, J.W.: Real-time traffic light detection using color density. In: 2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), pp. 1–4, October 2016
Uijlings, J.R.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vis. 104, 154–171 (2013)
Wang, C., Jin, T., Yang, M., Wang, B.: Robust and real-time traffic lights recognition in complex urban environments. Int. J. Comput. Intell. Syst. 4, 1383–1390 (2011)
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Wang, C., Zhang, G., Zhou, W., Rao, Y., Lv, Y. (2020). Traffic Lights Detection Based on Deep Learning Feature. In: Li, B., Zheng, J., Fang, Y., Yang, M., Yan, Z. (eds) IoT as a Service. IoTaaS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 316. Springer, Cham. https://doi.org/10.1007/978-3-030-44751-9_32
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