Each year around 10 000 tons of plastic buoys, untethered from fishing nets, wash up on the shores of South Korea. As they disintegrate they add toxic micro plastics to the water devastating marine life and the livelihoods depending on them.

Manually identifying buoys and collecting them is time consuming and burdensome. There needed to be a way to make this process faster and better.

Although AI is being used in various sectors to increase productivity and efficiency, machine learning is yet to be implemeted in the plastic removal process.

Manually identifying buoys and collecting them was time consuming and burdensome. There needed to be a way to make this process faster and better. Thus machine learning technique was applied to solve this problem, systematically identifying and locating buoys on the coastline.

TensorFlow Lite performs analysis on photos of the buoys to extract key characteristics used to uniquely identify each object. This information is collected in Google's Firebase database where it is analysed and then overlaid on Google Maps. With this technique anyone with a smartphone can take a photo of litter on the coastline and contribute to plastic removal efforts.

Photos collected by individuals and drone footages coupled with TensorFlow Lite will allow for effective identification of buoys on coastline. Such technology will improve the speed and efficacy of litter removal.