Real-Time Waste Sorting/Classification using CV
In this use case, the system tackles the slow, dirty, and often dangerous process of manual waste sorting by instantly identifying and segmenting different types of trash. Every piece of garbage moving through the frame is detected and classified into distinct categories like plastic bottles, plastic containers, plastic bags, waste paper etc. Using segmentation masks, the model precisely outlines the boundaries of each item, making it highly effective for environments where waste is clustered or overlapping.
To achieve this level of accuracy, the model leverages RetinaMask, which provides high-fidelity, pixel-level prediction to handle the complex, deformed shapes that crushed bottles and torn plastic bags typically present. Everything overlays live on the video feed to provide a real-time sorting and classification dashboard.
High level workflow:
- Collected raw video footage of mixed waste including bottles, bags, containers, and paper.
- Trained a YOLO11 model with a custom augmented dataset (incorporating rotations and flips) to prevent overfitting and ensure robust detection of mangled waste.
- Implemented RetinaMask logic during inference for precise, high-resolution segmentation masks around complex shapes.
- Ran inference per frame to get bounding boxes, segmentation masks, and specific class labels (bottles, containers, bags, paper).
- Visualized the automated classification and segmentation masks as a live overlay on the raw video footage.
This kind of pipeline is useful for recycling center operators, automated waste sorting facilities, robotic sorting pipelines (guiding robotic arms for precise picking), and environmental tech teams looking to prevent contamination in recycling streams.