Case: Ocean Clean Up
River Plastic Monitoring Using Deep Learning and Cameras
For his thesis Colin van Lieshout researched the concept of enabling automated monitoring of plastic in rivers for The Ocean Cleanup using deep learning. The Ocean Cleanup’s mission is to rid the world’s oceans and rivers of plastic pollution. Collecting data is essential to obtain adequate understanding of the problem.
The Challenge
Monitoring of river plastic is essential for enabling effective interception. Doing this manually is a tedious and expensive process. The Ocean Cleanup had been experimenting with using cameras to collect data. The challenge, however, is to automatically extract information from the images that the cameras collect. This is particularly challenging because of the complexity and diversity of the river surface and plastic shapes and sizes.
What has been done
To solve this problem, an Object Detection Neural Network was developed which can localize plastic pollution in images, and thus quantify it. To make this possible, first the hundreds of images containing thousands of plastic objects had to be labelled manually. Then, an architecture was selected, and the neural network was optimized in many iterations. Results have been published to Earth and Space Science in collaboration with Prof. dr. Eric Postma.
The result
With this model The Ocean Cleanup is now able to obtain way more data with less effort and expense than before. This enables them to make better informed decisions and gives them more leverage in communication with stakeholders.
Better cost control and higher customer satisfaction thanks to data
Better cost control and higher customer satisfaction thanks to data
“Thanks to smart data, we can better estimate how many staff we need to schedule based on the weather forecast. That is good for our cost control and for customer satisfaction.”
Jules Lauwerijssen, Royal Bakery and Lunchroom
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