When the user captures an image of recyclable waste using a camera and sends it, the YOLO model detects the recyclable waste from the photo and provides information about its type and location.
We have trained YOLOv8 utilizing the recycled garbage data from AI hub.
In order to enable rapid operation with small memory on the Lambda server, it was deployed in ONNX format.
At present, there are 15 types of recyclable waste that can be detected.
These include furniture, scrap metal, wood, ceramics, vinyl, styrofoam, glass bottles, clothing, bicycles, electronics, paper, cans, PET bottles, plastic, and fluorescent lights.
We developed an API using AWS Lambda, and resolved the cold start issue by setting the Amazon EventBridge Scheduler to invoke the lambda function every five minutes.
AI Server pipeline
plans
Construction of an automated process for AI model updates (CI/CD).
