IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v8y2022i5idhcseit2390274.html

AI Powered Garbage Detection System

Author

Listed:
  • Mehveen Mehdi Khatoon
  • P Meghana
  • P Malathi

Abstract

The aim of this research is to develop a smart waste management system using TensorFlow based deep learning model. It performs real time object detection and classification. The bin consists of several compartments to segregate the waste including metal, plastic, paper. Object detection and waste classification is done in TensorFlow framework with pre-trained object detection model. This program classifies an input image as clean/unclean. This can later be used to automatically send alerts to respective authorities when a street is found to be unclean. Once a street is found to be unclean, it automatically sends an email alert to the respective authorities who can then take action. It is impossible to manually identify streets that require cleaning at a given time. With "CCTV Street Garbage Detection and Alert System", authorities can get updates about the streets that are unclean.

Suggested Citation

  • Mehveen Mehdi Khatoon & P Meghana & P Malathi, 2022. "AI Powered Garbage Detection System," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 8(5), pages 317-320, October.
  • Handle: RePEc:jbh:ijsrcs:v8:y2022:i5:id:hcseit2390274
    Note: Article URL: https://ijsrcseit.com/CSEIT2390274
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/CSEIT2390274
    File Function: Article URL
    Download Restriction: no

    File URL: https://ijsrcseit.com/paper/CSEIT2390274.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v8:y2022:i5:id:hcseit2390274. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.