📷 Vision AI & IoT Stack for Multi-Purpose Real-Time Analysis

A real-time, IoT-enabled vision AI stack for diverse applications, such as building access control, wildlife tracking, hazardous object detection, crowd counting, or pollinator insect monitoring. Powered by edge-trained models for image classification and a Svelte-based dashboard for configuration, monitoring, and analytics. Designed for secure, scalable, and modular deployments to handle multiple vision-based tasks in a unified architecture.

🧱 Architecture Overview

The stack is built for real-time image and video analysis, IoT camera integration, and edge processing. All components are containerized for scalability and isolation, supporting varied use cases like facility security, environmental conservation, risk detection, and population analysis.

Layer	Component	Description
Frontend	Svelte Dashboard	Web-based dashboard for configuring models, monitoring camera feeds, and analyzing results across all use cases. Built with Svelte and Tailwind CSS.
IoT Integration	MQTT Broker (Mosquitto)	Manages real-time data streams from IoT cameras and devices to the processing engine.
Vision AI Engine	Edge-Trained Models	Lightweight models (e.g., YOLO, MobileNet, custom OCR) for real-time detection and classification tasks (e.g., access cards, wildlife, objects, crowds, insects).
Memory & Search	PostgreSQL + pgvector	Stores metadata, detection logs, and historical data for search and retrieval across all use cases.
Analytics	PostgreSQL	Logs detection results, counts, and metrics for dashboard analytics and reporting.
Infrastructure	Docker	Containerized services for edge or cloud deployments, ensuring scalability and isolation.

📡 IoT Camera Integration

The MQTT Broker (Mosquitto) enables real-time communication with IoT cameras:

Image/Video Streaming: Captures feeds from cameras for monitoring facilities, natural habitats, or public spaces.
Device Support: Compatible with RTSP, HTTP, or other camera protocols.
Scalability: Handles multiple cameras across diverse environments (e.g., corporate buildings, nature reserves, urban areas).
Reliability: Ensures robust data delivery with retry mechanisms.

👁️ Vision AI Engine

The edge-trained vision models power real-time detection and classification:

Model Types: Custom models (e.g., YOLO for object detection, OCR for text, MobileNet for classification) trained on datasets for access cards, wildlife, hazardous objects, crowds, and insects.
Edge Deployment: Runs on edge devices (e.g., NVIDIA Jetson, Raspberry Pi) for low-latency processing.
Multi-Purpose: Supports multiple tasks (e.g., access verification, wildlife monitoring, risk detection, counting) in a single stack.
Customizable: Models fine-tuned for specific conditions (e.g., low-light environments, rare species, urban settings).

🔍 Memory & Search

The PostgreSQL + pgvector layer enables:

Data Storage: Logs detection data (e.g., access records, wildlife sightings, risk alerts, crowd/insect counts) with timestamps and metadata.
Search Functionality: Allows dashboard users to query specific detections (e.g., access cards, species, or alerts) by time, location, or type.
Contextual Retrieval: Uses vector search for efficient lookup of historical data.
Privacy: Data is stored locally and auditable.

⚙️ Svelte Dashboard Configuration

The Svelte-based dashboard provides:

Model Configuration: Adjust detection thresholds, model types, and task-specific settings (e.g., sensitivity for risk detection, species for wildlife tracking).
Camera Feed Monitoring: View live feeds and real-time detections across all use cases.
Search & Query: Search for specific detections (e.g., access records, wildlife sightings, or alerts).
Analytics: Visualize metrics (e.g., access logs, wildlife populations, crowd/insect counts, security alerts).
Security: Role-based access for admins, security teams, conservationists, and researchers.

🛡️ Security & Deployment

Containerized: Dockerized services for isolation and portability.
Secure Communication: MQTT with TLS for encrypted camera data streams.
Data Privacy: Local storage in PostgreSQL, GDPR-compliant.
Auditable Logs: Tracks all detections and searches for compliance and auditing.
Deployment Options: Edge (on-site for facilities or natural sites), cloud (AWS, Azure), or hybrid, air-gapped compatible.

📊 Example Use Cases

Use Case	Configuration Example	Description
Building Access Control	Access card detection, entry logging	Verifies identification cards for building access, logs entry events.
Wildlife Tracking	Species identification, habitat mapping	Detects animals in protected areas for biodiversity research.
Hazardous Object Detection	Suspicious object detection, real-time alerts	Identifies items like unattended bags or weapons, alerts security teams.
Crowd Counting	Person detection, event monitoring	Counts visitors in public spaces for capacity planning or event management.
Pollinator Insect Monitoring	Insect detection, population analysis	Tracks bees or other pollinators for environmental studies.
Vehicle Speed Monitoring	License plate detection, speed estimation	Detects vehicle plates and estimates speed for traffic management.
Waste Sorting	Material classification, recycling analysis	Identifies recyclable materials in waste streams for sorting automation.
Crop Health Assessment	Plant disease detection, growth monitoring	Detects signs of disease or stress in crops for agricultural optimization.

✅ Why This Stack?

Multi-Purpose Vision: Handles diverse tasks (access control, wildlife tracking, risk detection, counting) in a unified stack.
Real-Time Processing: Fast, edge-based detection with custom-trained models.
IoT Camera Integration: Seamless connectivity with cameras via MQTT.
Searchable Database: Efficiently query detections with PostgreSQL and vector search.
Svelte Dashboard: Lightweight, responsive UI for monitoring, search, and analytics.
Secure & Scalable: Enterprise-grade, containerized, and privacy-focused.

💼 Our Services

We provide end-to-end support:

Model training on your datasets for access cards, wildlife, objects, crowds, insects, vehicles, waste materials, and crops
IoT camera integration and MQTT setup
Custom Svelte dashboard implementation
Database configuration for search and analytics
Team training and ongoing support

Vision AI & IoT Stack