Waste detection in Pomerania: non-profit project for detecting waste in environment
This project addresses waste management for environmental and recycling industries, but it is incremental as it applies existing AI methods to a specific domain.
The researchers tackled waste pollution by developing an open-source framework using two neural networks for detecting and classifying litter into seven categories, achieving up to 70% average precision in detection and around 75% classification accuracy on test data.
Waste pollution is one of the most significant environmental issues in the modern world. The importance of recycling is well known, either for economic or ecological reasons, and the industry demands high efficiency. Our team conducted comprehensive research on Artificial Intelligence usage in waste detection and classification to fight the world's waste pollution problem. As a result an open-source framework that enables the detection and classification of litter was developed. The final pipeline consists of two neural networks: one that detects litter and a second responsible for litter classification. Waste is classified into seven categories: bio, glass, metal and plastic, non-recyclable, other, paper and unknown. Our approach achieves up to 70% of average precision in waste detection and around 75% of classification accuracy on the test dataset. The code used in the studies is publicly available online.