1. Project Advisor: Dr. Jerry Gao Dr. Xuan Guan
Autonomous UAV Forced Graffiti Detection and Removal System Based on Machine Learning
Project Advisor: Dr. Jerry Gao Dr. Xuan Guan
Team:
Prakhar Nahar
Kang-hua Wu
Sitao Mei
Hadiyah Ghoghari
Yueh-lin Lee
San Jose State University

2. Introduction - Problem
Graffiti has had a major impact on the City of San Jose in recent years with the increasing coverage.
An estimate of 1,500,000 square feet of graffiti needing to be cleaned up every year.
In 2011, the Department of Parks, Recreation and Neighborhood Services (PRNS) spent $800,000 in compensation for graffiti clean-up.
Graffiti removal on highway overpasses is currently an expensive and time-consuming process, requires highway closures, leading to an impact on the economy in the range of millions of dollars.
Currently, removal projects require the use of several cherry picker machines and a few dozen personnel or crew for the cleanup process

3. Introduction - Solution
Proposed Solution:
a graffiti cleanup system which contains hardware and software that allow the users to use spray enamel with the reach and scale of an autonomous UAV
Automated using navigation and graffiti recognition algorithm.
Less time.
Cost Effective.
Public contribution via mobile (crowd sourcing).
Self Learning (Machine Learning)
Mobile.

4. Overall System and Mission Flight Profile

5. System Architecture

6. Components of UAV Communication and User Interface Navigation
Graffiti Detection
Spray Controller

7. Communication and User Interface

8. Navigation

9. Graffiti Detection

10. Graffiti Detection

11. Spray Mechanical Trigger
Spray Controller
Spray Controller
Spray Mechanical Trigger