Detect image objects, crop, and index in Elasticsearch

This automation workflow enables semantic analysis of images by detecting objects, cropping them out, and indexing them in Elasticsearch with metadata — enabling fast, content-based image search. Designed for developers and systems requiring scalable visual data processing.

## Who it´s for
- Developers building image search systems
- Companies automating visual content cataloging
- Analysts using semantic image analysis
- SaaS products with object-based image search features

## What the automation does
- Manually triggered with an image URL input
- Uses Cloudflare AI’s object detection model to identify items in the image
- Filters detections by confidence score (≥ 0.9)
- Crops each detected object from the original image
- Uploads cropped images to Cloudinary
- Indexes objects in Elasticsearch with labels, coordinates, and URLs

## What´s included
- Ready-to-use n8n workflow
- Manual trigger and image processing logic
- Integrations with Cloudflare AI, Cloudinary, and Elasticsearch
- Basic setup and adaptation guide

## Requirements for setup
- n8n instance (self-hosted or cloud)
- Cloudflare Workers AI access with object detection model permissions
- Cloudinary account for storing cropped images
- Elasticsearch instance for indexing
- Valid API keys and service URLs

## Benefits and outcomes
- Automated image annotation without manual labor
- Build searchable visual databases
- Accelerate product and content cataloging
- Scale image libraries efficiently
- Support integration into SaaS platforms needing visual analysis

## Important: template only
Important: you are purchasing a ready-made automation workflow template only. Rollout into your infrastructure, connecting specific accounts and services, 1:1 setup help, custom adjustments for non-standard stacks and any consulting support are provided as a separate paid service at an individual rate. To discuss custom work or 1:1 help, contact via Telegram: @gleb923.