Doko combines computer vision-based waste classification with an interactive Unity experience. The project is presented as an educational application focused on recycling and waste sorting.
Visual notes
Inside the project.
Doko mobile waste-learning experienceDoko waste-classification learning interfaceDoko team collaborating during an online session
02 / Approach
How it is put together.
01
Waste classification
A deep learning model supports AI-assisted classification of waste items.
02
Model integration
ONNX is included in the technology stack for model integration.
03
Interactive learning
Unity provides the interactive experience around the waste-classification concept.
03 / Outcomes
Signals of impact.
Figures and recognition are presented as recorded in the project information; measurement details should be included where available.
300+.
Children involved in testing
The project brief reports testing with more than 300 children in Bandung.
70%.
Reported skill improvement
The project brief reports a 70% improvement in recycling skills.
14 teams.
Indonesian representation
The project was associated with the 2024 Google Solution Challenge and the group of 14 Indonesian teams.
Project takeaway
What came out of it.
The project brief records testing with more than 300 children in Bandung and a reported 70% improvement in recycling skills. It also notes recognition among 14 Indonesian teams in the 2024 Google Solution Challenge. Evaluation details should be linked where available.
