---
title: "Image Annotation for Ore Detection"
description: "How do you polygon-annotate conveyor video where each frame is packed with moving ore? Split the frames, annotate in parallel, merge at validation."
url: "https://unidata.pro/cases/image-annotation-for-ore-detection/"
date_modified: "2026-05-15T07:45:18+03:00"
language: "en-US"
---
### Video Annotation for Ore Detection

We helped a mining company train a model to detect ore granularity and oversized fragments directly from conveyor belt video streams—reducing processing delays and removing the need for internal QA involvement.

The Task
--------

The client required annotated video data to train a model capable of detecting ore fragment sizes and identifying oversized pieces in real time on a conveyor belt.

The main challenge was handling dense scenes with multiple moving objects, where each frame required precise polygon-based annotation. Previous vendors failed to ensure consistent validation, forcing the client to rely on their internal team.

The deadline was strict: the full annotation and QA cycle had to be completed within 1.5 weeks.

The Solution
------------

We designed a high-speed video annotation pipeline with strong QA control:

### Rapid Team Setup & Workflow Optimization:

We assembled a team of 13 annotators within 24 hours. Due to heavy polygon load per frame, we split video frames into segments, annotated them separately, and merged them for final validation.

### Frame-by-Frame Annotation:

Each frame was annotated with detailed polygons to capture ore fragments and identify oversized pieces, ensuring temporal consistency across sequences.

### Multi-Level Validation:

Each batch passed through several QA layers. Feedback loops between annotators and validators were minimized through direct communication, while internal experts handled all edge cases.

| Stage | Input | Workflow Scope | Main Quality Checks |
|---|---|---|---|
| Requirements Alignment | Client goals, conveyor belt videos | Definition of object classes and size criteria | Clarity, edge cases, feasibility |
| Workflow Optimization | Raw video data | Frame splitting, workload distribution | Processing speed, system stability |
| Frame Annotation | Video frames | Polygon annotation of ore fragments | Boundary accuracy, temporal consistency |
| Validation | Annotated sequences | Multi-level QA, batch review | Frame-to-frame consistency, error rate |
| Final QA | Validated dataset | Merging segments, dataset delivery | Completeness, client acceptance |

## Hero

**Industry and Use Case:** Mining and Oil & Gas Industry **Data:** 300 annotated ore images **Project Duration:** 2 weeks

## Main Title

Image Annotation for Ore Detection

## Description

We helped a mining company quickly train a model to detect ore granularity and oversized fragments directly on the conveyor belt—cutting processing delays and freeing up internal resources.

## Progress - Results - Quote

### Progress - Steps

**List of Steps:**

- **Number of days:** Pilot & Sampling — **Step Description:** 2 days
- **Number of days:** Guidelines & Metrics Alignment — **Step Description:** 2 days
- **Number of days:** Video Annotation — **Step Description:** 7 days
- **Number of days:** QA & Final Dataset Delivery — **Step Description:** 3 days

### Results

**List of Results:**

- Full annotation and validation completed in 1.5 weeks
- No need for client-side QA or annotation involvement
- Production-grade dataset delivered with high consistency and accuracy

### Quote

**Quote:** Video annotation for industrial environments demands consistency across frames and precise handling of moving objects. High-quality datasets depend on optimized workflows, frame-level accuracy, and tightly integrated quality control.

**Author:** Roman Lukoshin

**Position:** Speech and Generative Data Manager

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