Traffic Congestion detection using CNN and Object detection

By MarksMaster
Python, Machine Learning, CNN, Object detection, Computer Vision
Expert, Bachelors/Undergraduate, Masters/Postgraduate
Activity, Assessment, Homework, Project

Project Brief: Congestion Detection System for Road and Highways Using Pretrained CNN and Object Detection

Overview: The Congestion Detection System is a Python-based solution designed to monitor and detect traffic congestion on roads and highways. The system utilizes a pre-trained Convolutional Neural Network (CNN) to identify vehicles in images and employs object detection techniques to analyze traffic conditions. This system has potential applications in traffic monitoring, GPS navigation, and drone-assisted traffic management.

Key Features:

  1. Image Collection: The system captures real-time images from various sources, such as cameras placed along roads, drones, or other image providers.

  2. Vehicle Detection: A pretrained CNN is employed to identify vehicles within the captured images. The CNN is trained to recognize common vehicle shapes, making it suitable for identifying cars, trucks, and other relevant vehicles.

  3. Object Detection: Object detection techniques are utilized to locate and delineate individual vehicles within the images. This involves drawing bounding boxes around each detected vehicle for accurate identification.

  4. Congestion Analysis: By analyzing the density and spatial distribution of detected vehicles, the system determines the level of congestion on the road. Congestion is inferred based on factors such as the average distance between vehicles, vehicle speed, and the proportion of the road covered by vehicles.

  5. Real-time Updates: The system provides real-time updates on traffic conditions, allowing users to make informed decisions about route selection, estimated travel time, and alternative routes.

  6. Integration with GPS Systems: The congestion data can be integrated into GPS navigation applications, providing users with route recommendations that help avoid congested areas and reduce travel time.

  7. Drone Assistance: Drones equipped with cameras can be employed to capture images from above, enhancing the system's coverage and accuracy. Drones can also assist in monitoring traffic flow in remote or inaccessible areas.


  1. Data Collection: The system acquires images from various sources, including roadside cameras, drones, and other available imagery.

  2. Vehicle Detection: The pretrained CNN model is applied to the acquired images to identify vehicles. The CNN has been trained on a dataset of vehicles and can accurately distinguish them from the background.

  3. Object Detection: Object detection algorithms, such as YOLO (You Only Look Once) or Faster R-CNN (Region-based Convolutional Neural Network), are employed to locate and draw bounding boxes around detected vehicles.

  4. Congestion Analysis: The system uses spatial analysis techniques to calculate vehicle density, average distance between vehicles, and other congestion indicators. These metrics are used to classify the congestion level.

  5. Real-time Updates and Integration: The system presents congestion information to users through a user-friendly interface, allowing them to make informed decisions. Congestion data can also be integrated with GPS navigation systems.

  6. Drone Integration: Drones equipped with cameras can be programmed to fly over congested areas and capture images, which are then processed by the system to assess traffic conditions from an aerial perspective.


  • Efficient Travel: Users can optimize their routes based on real-time congestion data, reducing travel time and fuel consumption.

  • Traffic Management: Authorities can use the system to identify congestion-prone areas and implement targeted traffic management strategies.

  • Data-driven Decisions: The system empowers decision-making by providing reliable congestion information to users and authorities.

  • Scalability: The system can be easily expanded to cover larger areas and integrate with various data sources.

The project Comes with code, data, and output screenshot
And most important code is dataset independent you can use any car dataset to train it

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