What If We Could Understand Waste Before We Collect It? Building WasteOps

Tags: machinelearning computervision python sustainability

What if a photograph of a waste bin could tell us more than just what we're looking at?

What if it could eventually help us understand material composition, identify contamination, estimate bin occupancy, and make better collection decisions?

That question led me to build WasteOps, an AI-powered waste intelligence platform.

This isn't just an attempt to classify waste images. The larger goal is to explore how computer vision, machine learning, and predictive analytics can make waste management more measurable and intelligent.

Here's what I've built so far, what the initial results look like, and where I want to take the project.

1. The problem

Waste management involves a surprising amount of uncertainty.

Consider a few everyday questions:

• What materials are actually entering a waste stream?
• How much recyclable material is being lost?
• Are waste streams contaminated?
• How full are collection bins?
• When should a bin be collected?

Answering these questions often requires manual inspection, specialized infrastructure, or fragmented data.

I wanted to investigate whether a software-first approach using ordinary visual data could help address some of these challenges.

That's where WasteOps comes in.

2. What is WasteOps?

WasteOps is an evolving waste intelligence platform built around computer vision and machine learning.

The long-term vision is to transform visual waste observations into useful operational information.

The platform is being developed around five capabilities:

These are development goals. The first release focuses on the machine-learning foundation.

3. Building the first model

For WasteOps v1.0, I chose MobileNetV3 as the initial architecture.

The motivation was to start with a relatively lightweight convolutional neural network that could provide a practical baseline for waste-image classification.

The model was trained using the UCI RealWaste dataset.

Dataset: https://archive.ics.uci.edu/dataset/908/realwaste

The dataset contains real-world landfill images across nine categories.

The initial training workflow includes:

1. Dataset preparation and validation.
2. Train, validation, and test splitting.
3. Model fine-tuning.
4. Validation using macro-F1.
5. Final test evaluation.
6. Saving the trained checkpoint and evaluation metrics.

4. Initial results

The first training run produced these results:

The model reached a validation macro-F1 of approximately 0.8976 during training.

These results are encouraging for an initial baseline.

However, an important limitation needs to be acknowledged: the evaluation uses an image-level split. It does not establish how well the model generalizes to entirely unseen facilities, camera setups, or waste environments.

Improving this evaluation is one of my next priorities.

5. The technology behind it

The current implementation uses:

• Python for core development.
• PyTorch for model training and inference.
• Torchvision for computer-vision models and transformations.
• MobileNetV3 for image classification.
• FastAPI for the backend foundation.
• Scikit-learn for evaluation metrics.

The repository also includes an automated test suite and a reproducible training workflow.

6. Why image classification isn't enough

This is probably the most interesting part of the project for me.

Imagine taking a photograph of a bin containing plastic bottles, cardboard, food waste, and other materials.

A conventional image classifier may assign one overall category to that image.

But a useful waste-management system needs considerably more information:

• Which materials are present?
• What proportion of the visible waste belongs to each category?
• Are there materials that don't belong in that bin?
• How much of the bin's usable capacity is occupied?
• Based on previous observations, when might collection be required?

These are different technical problems.

Estimating material proportions in mixed scenes requires object detection or segmentation. Estimating bin occupancy requires suitable camera geometry and calibration. Collection forecasting requires repeated, timestamped observations.

I don't want to pretend that one model solves all these problems.

Instead, I want to build and validate each capability properly.

7. What's next?

The next stages of WasteOps will focus on four major technical directions.

A. Bin-fill estimation

Explore image-based occupancy estimation using calibrated bin geometry.

The objective is to estimate visible fill percentage, remaining capacity, and collection thresholds.

B. Mixed-waste composition

Investigate object detection and instance segmentation to identify multiple materials in the same scene.

This would move WasteOps beyond assigning a single label to an entire image.

C. Contamination detection

Explore how to identify misplaced materials using material-level predictions and waste-stream rules.

D. Collection forecasting

Build a forecasting baseline using repeated bin observations and estimated accumulation rates.

The objective is to move from reactive collection decisions toward data-informed scheduling.

E. Generalization

Evaluate the system under different lighting conditions, backgrounds, camera angles, and environments.

Ultimately, I want to understand whether these capabilities can work outside a controlled dataset.

8. The bigger question

The interesting challenge isn't simply building a model that recognizes waste.

It's exploring whether ordinary visual observations can become a reliable source of intelligence for waste-management systems.

Could a software-first platform help individuals, facilities, and waste-management businesses make better decisions without requiring expensive specialized hardware everywhere?

That's the direction I want to investigate with WasteOps.

9. Explore the project

WasteOps v1.0 is the beginning of a larger engineering and research effort.

GitHub repository:

https://github.com/sreevatsarajesh/Wasteops-AI

I'm particularly interested in feedback from people working in:

• Computer vision and machine learning.
• Waste management and recycling.
• Sustainability and climate technology.
• Intelligent infrastructure.

I'd love to hear your thoughts:

1. What datasets would you recommend for mixed-waste detection and segmentation?
2. How would you approach reliable bin-fill estimation using ordinary cameras?
3. Which capability would have the greatest practical impact: composition analysis, contamination detection, or collection forecasting?

If you're interested in the problem, feel free to explore the repository, open an issue, or get in touch.

WasteOps — Building toward a world where waste is understood, not just collected.