Find the Vehicles

Introduction
This application uses the power of deep learning and computer vision to detect and annotate vehicles within images.

How It Works

1. Upload an Image: Users can upload an image through the intuitive drag-and-drop interface.
2. API Call: Once an image is uploaded, it is sent to the FastAPI backend, which processes the image using a deep learning model based on YOLOv5 and trained through transfer learning.
3. Receive Predictions: The model returns the detected vehicles along with their categories, confidence levels, and bounding box coordinates.
4. Display Results: The frontend displays the annotated image with bounding boxes and labels. Detailed information about each detected vehicle, including the category, confidence level, and coordinates, is shown below the image.

Use Cases
The app aims to provide an easy-to-use interface for vehicle detection tasks, which in turn are suitable for various applications such as:

1. Security:
   • Traffic Monitoring: Monitor traffic and detect any suspicious vehicles in real-time.
   • Parking Surveillance: Integrate with parking lot surveillance systems to detect unauthorized vehicles or to manage parking space occupancy efficiently.
2. Retail Analytics:
   • Customer Behavior Analysis: Retailers can use vehicle detection to analyze customer behavior, such as identifying peak times and understanding shopping patterns based on vehicle influx.
   • Advertisement Effectiveness: Measure the impact of advertisements placed near roadways by tracking the number of vehicles passing by.
3. Automated Image Tagging:
   • Digital Asset Management: Automatically tag vehicle images in large digital asset libraries, making it easier to search and organize photos.
   • Content Moderation: Enhance content moderation systems by detecting and tagging vehicles in user-uploaded images across social media platforms.
4. Autonomous Driving:
   • Obstacle Detection: The model's detection capabilities can be integrated into autonomous driving systems to detect other vehicles on the road, aiding in navigation and collision avoidance.
   • Traffic Sign Recognition: The model can be extended to recognize traffic signs and signals, providing crucial information for autonomous vehicles to make informed driving decisions.

For more information, please visit the project's GitHub repository at https://github.com/gustavo-ribeiro-santiago/find-the-vehicles-app.

I hope you find this tool helpful and look forward to your feedback!