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Vehicle Detection and Counting using YOLOv5

This project is a vehicle detection and counting system using the YOLOv5 model. It uses a pre-trained model to detect various types of vehicles in a video file and stores the detected counts for analysis. The script is implemented in a Jupyter Notebook and uses the YOLOv5 model to detect objects in a given video. The results are saved, and video processing, vehicle counting, and result visualization are implemented in this project.

Features

  • Vehicle detection in a video using a YOLOv5 model.
  • Counting detected vehicles of different classes.
  • Visualization of detection results.
  • Saving processed video with vehicle detections.

Prerequisites

  • Python 3.9+
  • CUDA-compatible GPU (for running YOLOv5 on GPU)
  • Jupyter Notebook (if you prefer running the script interactively)

Python Packages

The following Python packages are required:

  • torch
  • opencv-python
  • yolov5 (installable from the official YOLOv5 GitHub repository)
  • numpy
  • ipython

Running

    1. Clone the repository
    1. Prepare the Jupyter Notebook
    1. Run all jupyter cells