Bin Fill Level Estimation - Wortel AI annotation portfolio

Sample frames for this project are available under NDA. Write to bilal@wortel.ai and we will share the relevant before and after pairs.

Challenges

Keeping fill level estimation consistent across thousands of images.
Camera angles, lighting conditions and bin types varied widely.
Partially filled bins had to be estimated with high accuracy.
Filename consistency had to match the client's naming convention exactly.

Project overview

A dataset preparation project supported an AI-based bin fill level estimation system. The task involved reviewing waste bin images manually, estimating the percentage of bin occupancy, and updating each image filename with the corresponding fill level.

Each image was inspected visually and the estimated fill percentage was appended to its filename following the client's naming convention, ranging from empty bins at nought per cent through to overflowing bins above one hundred per cent. The prepared dataset was used for machine learning model development.

Dataset

Images: 10,000+
Classes: Not stated
Annotation: Fill level estimation and filename tagging
Source: Client-provided
Tool: Manual image review and file renaming
Client: SmartEnd

Industry

Waste Management

Annotation type

Image Classification Image Tagging

Labels

0% (Empty)10%25%50%75%90%100% (Completely Full)Above 100% (overflow)

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