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We use the YOLO algorithm to detect garbage on streets and then use an IBM database to mark the locations we found garbage and mark it on a map.

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mihika-shrivastava/garbage-detection

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Garbage Detection: Leading the way to cleaner cities.

Content

  • Submission/Project name
    • Content
    • Short Description
      • What is the problem?
      • How can technology help?
      • The idea
  • Presentation Video
  • The Architecture
  • Long Description
  • Demo
  • Resources Used
  • Team Members

Short Description

What is the problem?

The world generates 2.01 billion tonnes of municipal solid waste annually, with at least 33 percent of that managed in an environmentally unsafe manner. According to estimates, by 2050, the global waste is expected to grow to about 3.40 billion tonnes, which will be more than double of population growth over the same period of time.
In India, due to rapid urbanisation, the country is facing massive waste management challenge. Over 377 million urban people live in 7,935 towns and cities and generate 62 million tonnes of municipal solid waste per annum. Only 43 million tonnes (MT) of the waste is collected. From that, 11.9 MT is treated and 31 MT is dumped in landfill sites. Solid Waste Management (SWM) is one among the basic essential services which must be provided by municipal authorities in the country to keep urban centres clean. However, almost all municipal authorities deposit solid waste at a dumpyard within or outside the city haphazardly. In these areas with polluted environments and ineffective waste management, abdominal diseases like Japanese Encephalitis, Jaundice, Cholera, Colitis, etc., are quite common.

How can technology help?

The key to efficient waste management is to ensure proper segregation of waste at source and to ensure that the waste goes through different streams of recycling and resource recovery. But as mentioned above, 33% of the mis-managed waste is the one which doesn't even reach the appropriate channel. Rather, it accumulates in local dumps which end up becoming quite filthy and becomes a breeding ground for infections and diseases. Quite often, Municipalities and the concerned authorities don't even know about the existance of many such dumping grounds in and around the city.

The idea

As the country gets more digitally connected, more and more people order commodities (like food) to be directly delivered to their houses. This results in a lot of delivery-people driving into neighbourhoods in and around the city. If we were to fit a smart camera in association with an inference engine in these vehicles, we could get the location of many local garbage dumps and thereby inform the concerned authorities to take suitable action.
We can also further use these techniques of artificial learning and neural networks to identify and segregate the waste so that they can be properly disposed.

Presentation Video

Link to the UPDATED youtube video: Call for Code submission Link to the previous youtube video: Wit Hackathon submission

The Architecture

Long Description

More detail is available here

Project Roadmap

Our project detects trash on streets through a hardware unit called an inference engine hard coded with a machine learning algorithm. It marks the locations where trash is detected on a map.

Demo

The object detection algorithm on a still image:

The object detection algorithm on a video:

The location of sites with garbage marked on a map with the help of its location coordiates taken from an IBM database:

Resources Used

  1. for the image dataset: https://www.kaggle.com/asdasdasasdas/garbage-classification
  2. for annotating the images: https://cloud.annotations.ai/
  3. for the database to store map coordinates: https://www.ibm.com/in-en/cloud/databases

Team Members


Mihika Shrivastava ([email protected])
Gomathi Raveendran ([email protected])
Kaaviya Ramesh ([email protected])
Swetha Manukonda ([email protected])

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We use the YOLO algorithm to detect garbage on streets and then use an IBM database to mark the locations we found garbage and mark it on a map.

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