Project Details

The Challenge | Trash Cleanup

Oceanic garbage patches are collections of marine debris that come together due to ocean currents; they have devastating effects on ocean ecosystems. Your challenge is to design a mission to help clean up garbage from the ocean!

Trash_Collector

Objective of project Trash_collector is using dataset from NASA for tracing the garbage patches, monitoring garbages and gathering them to the main collector box.

ROAR

Earth’s ocean has been polluted in many ways since very long and this deadly incident is still going on which endangered the life of aquatic plants and animals. The main focus of our project is cleaning the garbage patches of the ocean.

The project consists of two parts as main collector box and trash collector. The main collector will deploy the trash collectors to collect trashes from the oceanic bodies. Trash collectors will have camera with them which will be continuously sending pictures to the main collector box. The collector box will then using its AI detect the images whether they are trashes or not and send the command to the trash collectors. The trash collectors will then engulf the trashes and release the water taken during engulfing trashes. This will make sure that the trash collector has only the trashes and no water. Then once they are full, they will go to the main collector box to dump their part of trashes. This way all the trash collectors will continue doing so.

In future, we are planning to have a drone over the oceanic area which will be monitoring the oceanic areas. It will then send the detected images to the main collector. The main collector will then precede the further procedures of collecting trashes that is giving command to the trash collectors.

Resources:

      1. https://github.com/ahmunnaah/TRASH_COLLECTOR
      2. https://github.com/EdjeElectronics/TensorFlow-Obje...
      3. https://github.com/EdjeElectronics/TensorFlow-Obje...
      4. https://podaac.jpl.nasa.gov/Altimetric_Data_Information

      Hardwares:

      1. Raspberry pi 3b
      2. Web cam