Traffic Signal Recognition Using End-to-End Deep Learning
Publication: Tran-SET 2022
ABSTRACT
Traffic signal recognition is a core task in autonomous driving. In this paper, an end-to-end, real-time deep learning object detection algorithm called YOLO (you only look once) is adopted for traffic signal recognition applications. Two separate open-source image data sets were used for training signal detection models for day and night-time traffic scenarios, respectively. Since the default YOLO framework offers real-time detection, focus was given on achieving acceptable levels of detection accuracy through improving data quality for training. First, the training data sets were cleansed of existing ground truth annotation errors and irregularities. Second, by applying mosaic image augmentation, stitching four randomly upscaled images together, twice during the model training, overall detection accuracy was further improved.
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REFERENCES
Behrendt, K., L. Novak, and R. Botros. 2017. “A deep learning approach to traffic lights: Detection, tracking, and classification.” IEEE Int. Conf. Robot. Autom., 1370–1377. Singapore.
Du, L., W. Chen, S. Fu, H. Kong, C. Li, and Z. Pei. 2019. “Real-time detection of vehicle and traffic light for intelligent and connected vehicles based on YOLOv3 network.” 5th Int. Conf. Transp. Inf. Saf., 388–392. Liverpool, UK.
Jensen, M. B., K. Nasrollahi, and T. B. Moeslund. 2017. “Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data.” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., 882–888.
Jensen, M. B., M. P. Philipsen, A. Møgelmose, T. B. Moeslund, and M. M. Trivedi. 2016. “Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives.” IEEE Trans. Intell. Transp. Syst., 17 (7): 1800–1815. https://doi.org/10.1109/TITS.2015.2509509.
Jocher, G. n.d. “GitHub - ultralytics/yolov5.” Accessed June 1, 2022. https://github.com/ultralytics/yolov5.
Li, Z., Q. Zeng, Y. Liu, J. Liu, and L. Li. 2021. “An improved traffic lights recognition algorithm for autonomous driving in complex scenarios.” Int. J. Distrib. Sens. Networks, 17 (5). https://doi.org/10.1177/15501477211018374.
Liu, S., L. Qi, H. Qin, J. Shi, and J. Jia. 2018. “Path Aggregation Network for Instance Segmentation.” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 8759–8768. https://doi.org/10.48550/arxiv.1803.01534.
Peng, J., M. Xu, and Y. Yan. 2021. “Automatic Recognition of Pointer Meter Reading Based on Yolov4 and Improved U-net Algorithm.” IEEE Int. Conf. Electron. Technol. Commun. Inf., 52–57. Changchun,China.
Possatti, L. C., R. Guidolini, V. B. Cardoso, R. F. Berriel, T. M. Paixão, C. Badue, A. F. De Souza, and T. Oliveira-Santos. 2019. “Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars.” Int. Jt. Conf. Neural Networks (IJCNN). Budapest, Hungary.
Wang, C. Y., H. Y. Mark Liao, Y. H. Wu, P. Y. Chen, J. W. Hsieh, and I. H. Yeh. 2019. “CSPNet: A New Backbone that can Enhance Learning Capability of CNN.” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., 1571–1580. IEEE Computer Society.
Wang, Q., Q. Zhang, X. Liang, Y. Wang, C. Zhou, and V. I. Mikulovich. 2022. “Traffic Lights Detection and Recognition Method Based on the Improved YOLOv4 Algorithm.” Sensors, 22 (200). https://doi.org/10.3390/S22010200.
Yan, S., X. Liu, W. Qian, and Q. Chen. 2021. “An End-to-End Traffic Light Detection Algorithm Based on Deep Learning.” Int. Conf. Secur. Pattern Anal. Cybern., 370–373.
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Tran-SET 2022
Pages: 182 - 191
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Published online: Dec 13, 2022
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