Skip to content

Latest commit

 

History

History
45 lines (30 loc) · 1.72 KB

README.md

File metadata and controls

45 lines (30 loc) · 1.72 KB

CleanCampus

Welcome to CleanCampus, an AI-Powered Solution to prevent littering.

Problem

  • Littering is a very serious problem, it can lead to an increase in global warming.
  • Countries like the US spend an estimated 11.5 Billion Dollars on their efforts to clean litter.

Solution

  • We have trained a YOLOv8m model to detect a person and the litter they are throwing. If littering activity is detected, the face of the person is sent to a database, matched with an existing person's face, and then fined accordingly.
  • ByteTrack algorithm is used to track the persons and the litter.

Litter Classes

  • Paper
  • Cardboard
  • Can
  • Plastic

Training Examples

  • About 1500

Technologies

  • YOLOv8m Object Detection Model
  • ByteTrack Tracking Algorithm
  • MySQL for the Database
  • image_recognition of Python for the computation of the face embeddings
  • Tkinter for the application
  • Flask for connecting the model to the Application

Future

  • The application paves and proposes promising ways to detect litterbugs; however, more real-world training datasets will be required to bring the model up to mark for real-world use cases.
  • Making better UI and application for the admin, and adding additional features.
  • Deploying the model on CCTV.

Additional Notes

  • If you have an NVIDIA-GPU enabled machine, make sure to install the CUDA library to enable GPU inference.
  • Make sure to have dlib installed for the image_recognition library.
  • Make sure that you are in the same directory as the script that is going to be executed, otherwise you might get a path error, for example, if you are executing the code for detection and tracking, make sure that you are in the directory DetectionAndTracking, according to your terminal.