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Smart Cities

AI Smarter Spaces - Traffic Monitoring

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Challenge

Traditional traffic monitoring frameworks fall short in their ability to gather in-depth data on vehicle characteristics, leading to obstacles in effective traffic management and insightful analysis. The lack of capabilities for real-time, precise vehicle detection and analysis plays a significant role in causing road congestion, elevating accident frequencies, and resulting in inefficient traffic control. Such an innovative system is essential, not just for streamlining traffic and enhancing road safety, but also for mitigating environmental impact by improving traffic flow and decreasing pollution levels.

Solution

  • Collect The Vehicle image dataset: Gather different vehicle type images which are not defined by the model.
  • Collect the vehicle number plate, and brand recognition dataset: Collect different vehicle number plate image dataset and the car brand dataset.
  • Annotating the images with their respective labels: Using CVAT annotate the images and prepare them for image augmentation.
  • Image Augmentation: Augmenting the images for generating varieties of images using the original image dataset.
  • Fine-tune the models for training: Train two different models one on detecting the type of the vehicle and another one to detect the number plate position and the brand log on the car.
  • Inference and evaluation of the models: evaluate the models on the testing images and create an evaluation matrix of the fine-tuned the model .
Shape
BUSINESS BENEFITS

But, how will you take lead with these insights ?

Accurate vehicle data collection

Precise detection of vehicle types, colors, plates, brands.

Real-time traffic monitoring

Continuous monitoring and analysis of traffic patterns.

Improved traffic management

Data-driven insights for optimizing traffic flow and planning.

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