Our project is primarily focused on identifying marine debris in waterbodies. From our research we found out that much of the marine debris research focuses on floating plastic debris, but it is important to recognize that only approximately half of all plastic is positively buoyant, i.e., it floats. The rest of them sink underwater and likewise remain undetected. Moreover after some amount of time in the ocean, floating plastic debris may become sufficiently fouled with biological growth that the density becomes greater than seawater, and it sinks. Hence our prototype would primarily focus on detecting the surface concentration of marine debris as well as the debris that's has been living underwater. Through our model, we would also probably calculate the age of the plastic (i.e how long it has been on the sea). Basically our project is divided into two section : the hardware (the model that's structured in the form of a turtle and likewise would be released on to the water bodies) and the software (the server and user end part that would update the map information regarding the concentration and hotspots of marine debris)

Our hardware is developed through an AI route-mapped robot model that uses an OpenCV trained image data Tensorflow model to detect, monitor and quantify plastic waste under the ocean and report instances of found material as api queries to server. Live frames from the raspberry pi camera modules will be analyzed for signs of plastic waste from trained data on how plastics in the ocean will look like. Output data will then be recorded and saved on the raspberry pi model B storage device. The Quantified data will include picture frames of debris in the ocean ,which will be classified by the model and update it to our server. Our hardware model will be Integrated with the Saildrone(NASA) platform api for the sole purpose of sending parsed data from observational routines in the ocean to a server for live map showcase and analysis.

How we plan to make the data correlate with the experiments

Extensive data analysis was done on the dataset we found useful for the project. You can have a view of it here. All other information regarding the production of the code and robot is currently being shared on our devpost submission. Thank you for reading!