SteelVision: Pixel-Level Steel Defect Segmentation

Problem

Image-level pass/fail does not tell an inspector where a defect is. Small, low-contrast marks on textured steel are easy for a classifier to ignore and hard to show in a UI.

Solution

A segmentation pipeline with mask training, IoU/Dice evaluation, FastAPI inference, and a browser overlay so quality teams can inspect predicted defect regions.

What Dmitri Derevjanko built

Dmitri designed the dataset/mask workflow, trained UNet++ with an EfficientNet backbone, built the inference service and overlay UI, containerized the stack, and published the ai-vision repository.

Architecture

1. Aligned masks, augmentation, and train/validation splits for small defect regions.
2. UNet++ with EfficientNet backbone, segmentation losses, and checkpoint selection on IoU/Dice.
3. FastAPI accepts an image, runs no-grad inference, and returns a mask for overlay rendering.
4. Docker separates model artifacts from the service layer for a repeatable run.

Technologies

• PyTorch
• UNet++
• EfficientNet
• FastAPI
• OpenCV
• Docker

Result

A public segmentation demo and GitHub repo that show pixel-level defect localization with a simple upload-and-overlay workflow.

Limitations

• Trained for steel surface textures in the project dataset, not every alloy, coating, or camera setup.
• Class imbalance (tiny defect vs background) can still suppress faint marks.
• This is a technical demo for inspection experiments, not a certified factory QA gate.

Live demo and source

Live demo: https://ai-vision.dmitriderevjanko.com

GitHub: https://github.com/DmitriDerevjanko/ai-vision

Portfolio case study: https://dmitriderevjanko.com/projects/steelvision/

Other independent projects by Dmitri Derevjanko

• Law Agent
• Marketplace Intelligence
• OSINT Radar
• OncoView AI
• Global Risk Forecast
• SmartCam
• FinanceLens Analyst
• Estonian Company Intelligence Hub
• Opportunity Intelligence Studio