A Prototype for Mobile Application of Garbage Quantification using Mask R-CNN

Authors

  • Venugopal B Department of CSE, Gayatri Vidya Parishad College of Engineering(A), Visakhapatnam, AP, India
  • Chakravarthy LS Department of CSE, Gayatri Vidya Parishad College of Engineering(A), Visakhapatnam, AP, India
  • Praveen L Director of Peritus Technologies, It Sez, Madhurawada, Visakhapatnam, AP, India
  • Dwivedi AK Department of CSE, Gayatri Vidya Parishad College of Engineering(A), Visakhapatnam, AP, India

DOI:

https://doi.org/10.26438/ijcse/v6i11.617621

Keywords:

Garbage Quantification, Garbage Detection, Deep Learning, Computer Vision, Convolutional neural networks

Abstract

In cities especially some areas have garbage regions, So people are affected by several health issues. The main problem is authorities will not clean on time due to lack of information. Sometimes, authorities have also highly impossible to track these areas. Garbage quantification is an important step in improving the cleanliness of the cities. This paper presents one mobile application for garbage images with GPS locations to send authorities directly. When the user clicks the garbage image using through this app, then it will send that image to the server for automatic garbage detection with quantification by using the deep learning in computer vision techniques. Convolutional Neural Network (CNN) algorithms will be used to garbage detection with quantification in an image for accurate results.

References

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Published

2025-11-18
CITATION
DOI: 10.26438/ijcse/v6i11.617621
Published: 2025-11-18

How to Cite

[1]
B. Venugopal, L. S. Chakravarthy, L. Praveen, and A. K. Dwivedi, “A Prototype for Mobile Application of Garbage Quantification using Mask R-CNN”, Int. J. Comp. Sci. Eng., vol. 6, no. 11, pp. 617–621, Nov. 2025.

Issue

Vol. 6 No. 11 (2018): IJCSE November Edition

Section

Research Article

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