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Traffic Control Device Detection

This model analyzes highway construction project imagery to automatically detect and identify traffic flow control devices.

40 credits (with a subscription)

80 credits (without a subscription)

Version 1.2
Free Trial available!
SafetyVision TFC
Traffic Control Device Detection
Fast Analysis
This model detects:
  • Concrete barriers
  • Cones
  • Construction safety fences
  • Drums
  • Jersey barriers
  • Longitudinal channelizing devices
  • Screens
  • Stationary crash cushions
  • Temporary traffic control signs
  • Barricades (Type 3)
Improved Accuracy
This model can accurately detect what the human eye may miss, including a range of traffic control devices, from cones, barricades, barriers, work vehicles, and safety fences, to automated flagger devices, floating water markers, stationary crash cushions, life float rings, and much more.
Annotated Images and Detailed Reports
Bounding boxes will appear around detected traffic control devices, enabling you to quickly locate where they are within the image. The annotated images are viewable directly from our platform, for the utmost convenience, or you can download them for later viewing. The model also generates a detailed report that lists each detection along with their confidence level for further analysis.

Required Inputs

  • PNG, JPG, or JPEG files

Note: This model is trained on oblique imagery, collected downward to an approximate 45 degree angle to the ground.

Expected Outputs

  • Annotated images
  • A summary report in XLSX format

The Traffic Control Device Detection model provides an easy-to-use yet powerful way to identify the traffic flow control devices used in your highway construction project. Designed for engineers and other professionals involved in highway projects, the model allows you to quickly identify any missing or mispositioned traffic control devices and quickly replace, secure, or repair them as needed. As traffic control devices are a key element of any highway construction project, this model provides you with a speedy way to check on the overall safety of your project.

Note: Current mAP (Mean Average Precision) score is 62%. Results will continue to be refined as the model is trained on more data.

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