research-article

Detecting Traffic Lights by Single Shot Detection

Authors:

Julian Müller,

Klaus DietmayerAuthors Info & Claims

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

Pages 266 - 273

https://doi.org/10.1109/ITSC.2018.8569683

Published: 04 November 2018 Publication History

Abstract

Recent improvements in object detection are driven by the success of convolutional neural networks (CNN). They are able to learn rich features outperforming hand-crafted features. So far, research in traffic light detection mainly focused on hand-crafted features, such as color, shape or brightness of the traffic light bulb. This paper presents a deep learning approach for accurate traffic light detection in adapting a single shot detection (SSD) approach. SSD performs object proposals creation and classification using a single CNN. The original SSD struggles in detecting very small objects, which is essential for traffic light detection. By our adaptations it is possible to detect objects much smaller than ten pixels without increasing the input image size. We present an extensive evaluation on the DriveU Traffic Light Dataset (DTLD). We reach both, high accuracy and low false positive rates. The trained model is real-time capable with ten frames per second on a Nvidia Titan Xp. Code has been made available at https://github.com/julimueller/tl_ssd.

References

[1]

M. Bach, S. Reuter, and K. Dietmayer. Multi-camera traffic light recognition using a classifying labeled multi-bernoulli filter. In 2017 IEEE Intelligent Vehicles Symposium (IV), pages 1045–1051, June 2017.

[2]

D. Barnes, W. Maddern, and I. Posner. Exploiting 3D semantic scene priors for online traffic light interpretation. IEEE Intelligent Vehicles Symposium, Proceedings, 2015-Augus: 573–578, 2015.

[3]

K. Behrendt, L. Novak, and R. Botros. A deep learning approach to traffic lights: Detection, tracking, and classification. In 2017 IEEE International Conference on Robotics and Automation (ICRA), pages 1370–1377, May 2017.

[4]

A. Canziani, A. Paszke, and E. Culurciello. An Analysis of Deep Neural Network Models for Practical Applications. CoRR, abs/1605.07678, 2016.

[5]

C.C. Chiang, M.C. Ho, H.S. Liao, A. Pratama, and W.C. Syu. Detecting and recognizing traffic lights by genetic approximate ellipse detection and spatial texture layouts. International Journal of Innovative Computing, Information and Control, 7 (12): 6919–6934, 2011.

[6]

R. De Charette and F. Nashashibi. Real time visual traffic lights recognition based on spot light detection and adaptive traffic lights templates. IEEE Intelligent Vehicles Symposium, Proceedings, pages 358–363, 2009.

[7]

M. Diaz-Cabrera, P. Cerri, and J. Sanchez-Medina. Suspended traffic lights detection and distance estimation using color features. In IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, pages 1315–1320, Anchorage, AK, 2012. IEEE.

[8]

P. Dollar, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: A benchmark. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 304–311, June 2009.

[9]

P. Dollar, C. Wojek, B. Schiele, and P. Perona. Pedestrian Detection: An Evaluation of the State of the Art. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34 (4): 743–761, April 2012.

Digital Library

[10]

D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov. Scalable Object Detection Using Deep Neural Networks. Cvpr, pages 2155–2162, 2014.

[11]

N. Fairfield and C. Urmson. Traffic light mapping and detection. In Proceedings of ICRA 2011, 2011.

[12]

A. Fregin, J. Müller, and K. Dietmayer. Feature detectors for traffic light recognition. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pages 339–346, Oct 2017.

[13]

A. Fregin, J. Müller, and K. Dietmayer. Three ways of using stereo vision for traffic light recognition. In 2017 IEEE Intelligent Vehicles Symposium (IV), pages 430–436, June 2017.

[14]

A. Fregin, J. Müller, U. Kressel, and K. Dietmayer. The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets. In 2018 IEEE International Conference on Robotics and Automation, ICRA 2018, Brisbane, Australia, May 21 - May 25, 2018, forthcoming.

[15]

J. Gong, Y. Jiang, G. Xiong, C. Guan, G. Tao, and H. Chen. The recognition and tracking of traffic lights based on color segmentation and CAMSHIFT for intelligent vehicles. 2010 IEEE Intelligent Vehicles Symposium, pages 431–435, 2010.

[16]

H. Kim, Y. Shin, S.-G. Kuk, J. Park, and H. Jung. Night-Time Traffic Light Detection Based On SVM with Geometric Moment Features. Waset.Org, 7 (4): 454–457, 2013.

[17]

J. Levinson, J. Askeland, J. Dolson, and S. Thrun. Traffic light mapping, localization, and state detection for autonomous vehicles. Proceedings - IEEE International Conference on Robotics and Automation, pages 5784–5791, 2011.

[18]

F. Lindner, U. Kressel, and S. Kaelberer. Robust recognition of traffic signals. In IEEE Intelligent Vehicles Symposium, 2004, pages 49–53, June 2004.

[19]

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, and S.E. Reed. SSD: Single Shot MultiBox Detector. CoRR, abs/1512.0(1), 2015.

[20]

W. Luo, Y. Li, R. Urtasun, and R. Zemel. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks. In Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS'16, pages 4905–4913, USA, 2016. Curran Associates Inc.

[21]

J. Müller, A. Fregin, and K. Dietmayer. Multi-camera system for traffic light detection: About camera setup and mapping of detections. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pages 165–172, Oct 2017.

[22]

M. Omachi and S. Omachi. Traffic light detection with color and edge information. In Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on, pages 284–287, Aug 2009.

[23]

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. You Only Look Once: Unified, Real-Time Object Detection.

[24]

J. Redmon and A. Farhadi. YOLOv3: An Incremental Improvement. CoRR, abs/1804.02767, 2018.

[25]

S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. pages 1–14, 2015.

[26]

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. Le-Cun. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks. 2013.

[27]

C. Szegedy, S. Reed, P. Sermanet, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. pages 1–12.

[28]

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. Rethinking the Inception Architecture for Computer Vision. CoRR, abs/1512.00567, 2015.

[29]

J. Uijlings, K. van de Sande, T. Gevers, and A. Smeulders. Selective Search for Object Recognition. International Journal of Computer Vision, 2013.

[30]

M. Omachi and S. Omachi. Detection of traffic light using structural information. Signal Processing (ICSP), 2010 IEEE 10th International Conference on, 2: 809–812, 2010.

[31]

M. Weber, P. Wolf, and J.M. Zöllner. DeepTLR: A single deep convolutional network for detection and classification of traffic lights. In 2016 IEEE Intelligent Vehicles Symposium (IV), pages 342–348, June 2016.

[32]

H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia. Pyramid Scene Parsing Network. 2016.

[33]

L. Zitnick and P. Dollar. Edge Boxes: Locating Object Proposals from Edges. In ECCV. European Conference on Computer Vision, September 2014.

Cited By

He XZheng XHao XJin HZhou XShao L(2024)Improving small object detection via context-aware and feature-enhanced plug-and-play modulesJournal of Real-Time Image Processing10.1007/s11554-024-01426-821:2Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s11554-024-01426-8
Hua HNguyen KQuach LTran H(2023)Traffic Lights Detection and Recognition Method using Deep Learning with Improved YOLOv5 for Autonomous Vehicle in ROS2Proceedings of the 2023 8th International Conference on Intelligent Information Technology10.1145/3591569.3591589(117-122)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3591569.3591589

Index Terms

Detecting Traffic Lights by Single Shot Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

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cover image Guide Proceedings

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

Nov 2018

3947 pages

ISBN:978-1-7281-0321-1

Copyright © 2018.

Publisher

IEEE Press

Publication History

Published: 04 November 2018

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Cited By

He XZheng XHao XJin HZhou XShao L(2024)Improving small object detection via context-aware and feature-enhanced plug-and-play modulesJournal of Real-Time Image Processing10.1007/s11554-024-01426-821:2Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s11554-024-01426-8
Hua HNguyen KQuach LTran H(2023)Traffic Lights Detection and Recognition Method using Deep Learning with Improved YOLOv5 for Autonomous Vehicle in ROS2Proceedings of the 2023 8th International Conference on Intelligent Information Technology10.1145/3591569.3591589(117-122)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3591569.3591589

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