The Warning System for Speed Cameras on the Road by Deep Learning

  • Chien-Chung WuEmail author
  • Yu-Xuan Lin
  • Deng-Xiang Hu
  • Chien-Chuan Ko
  • Ji-Han Jiang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)


In order to reduce traffic accidents, government authorities would install speed cameras for identifying vehicles travelling over the legal speed limit. However, drivers often ignored warnings and were handed tickets, and meanwhile it did not achieve the purpose of setting up the cameras. This paper took the road in Taiwan as an example. By installing a camera in front of the vehicle, it could capture the image of the road ahead. This system was implemented on the NVIDIA Jetson TX2 and used YOLO V3 as the architecture for CNN classification. The images were divided into six categories: “police patrol car”, “warning sign for front speed camera”, “warning slogan for front speed camera”, “front view of radar speed camera”, “back view of radar speed camera”, “traffic sign of speed limit”. When the first five categories were detected, the system would issue a warning, and when “the traffic sign of speed limit” was detected, the number in the middle part would be identified to obtain and update the speed limit. The result of the experiment on the YOLO V3 when outputting 6 categories was that Mean AP was 81.25%, and Average Recall was 85.5%. The result of the digital recognition for speed limit had a precision of 83.5% and a recall of 80.2%.


Convolutional neural network Embedded system Speed limit detection Speed cameras alert YOLO CNN 



This work was supported by the Taiwan Ministry of Science and Technology MOST 107-2221-E-218-023-MY2.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chien-Chung Wu
    • 1
    Email author
  • Yu-Xuan Lin
    • 1
  • Deng-Xiang Hu
    • 1
  • Chien-Chuan Ko
    • 2
  • Ji-Han Jiang
    • 3
  1. 1.Southern Taiwan University of Science and TechnologyTainanTaiwan
  2. 2.National Chiayi UniversityChiayiTaiwan
  3. 3.National Formosa UniversityYunlinTaiwan

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