Skip to content

Industrial Vision System

Overview

An exploration into developing a next-generation industrial vision system that addresses limitations in current factory safety camera solutions. The concept focuses on contextual intelligence using local vision language models to distinguish between different types of human activity in industrial settings.

Problem Statement

Current industrial vision systems have significant limitations:

  • Lack of Context: Cannot distinguish between pedestrians and forklift operators
  • High Cost: ~$15K for basic dual-camera forklift systems
  • Inflexibility: Vendors unwilling to customize software for specific use cases
  • Limited Intelligence: Simple detection without understanding of context or intent

Proposed Solution

Technical Architecture

Local AI Processing: - Moondream vision language model for contextual understanding - Prompt-based configuration for specific use cases - Local processing for privacy and reliability

Hardware Platform Options: 1. Mac Mini Approach - Built-in touchscreen support - Compact form factor - MLX optimization (waiting for Moondream 3 support)

  1. Custom Linux/GPU Build
  2. More cost-effective hardware
  3. Better GPU optimization for vision models

  4. Greater customization flexibility

  5. Android-Based System

  6. Touchscreen-native platform
  7. Custom Android fork capabilities
  8. Proven in embedded applications (drone industry)

User Interface: - Touch screen for on-site configuration - Setup wizard for easy deployment - Real-time monitoring and alerts

Business Model

Two-Tier Offering:

  1. Hardware Solution (~$7,500/device)
  2. Standalone vision system
  3. Local processing and alerts

  4. Custom configuration capabilities

  5. Subscription Service

  6. Data aggregation across multiple devices
  7. Proactive recommendations and insights
  8. Remote monitoring and analytics
  9. Alternative: Monthly per-device subscription model

Technical Implementation

Core Components

Vision Processing Pipeline:
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Camera Feed   │ -> │  Moondream VLM   │ -> │  Alert System   │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                              │
                              v
                    ┌──────────────────┐
                    │ Context Analysis │
                    │ (Prompt-based)   │
                    └──────────────────┘

Key Features: - Real-time video processing - Customizable alert conditions via natural language prompts - Local storage and processing for reliability - Optional cloud connectivity for analytics

Development Roadmap

Phase 1: MVP Development - iOS app using Moondream cloud API - TestFlight deployment for real-world testing - Validation in actual factory environment

Phase 2: Hardware Integration - Local Moondream deployment - Hardware platform selection and testing - Touch interface development

Phase 3: Production System - Ruggedized hardware design - 12V power integration for forklift mounting - Setup wizard and configuration tools

Phase 4: Subscription Platform - Multi-device data aggregation - Analytics and reporting dashboard - Proactive recommendation engine

Market Analysis

Target Applications

Forklift Safety: - Front/rear pedestrian detection - Context-aware alerts (pedestrian vs. operator) - Mounting and power integration requirements

Stationary Monitoring: - Restricted area access monitoring - Equipment safety compliance - Workflow optimization insights

General Factory Safety: - Personal protective equipment verification - Dangerous behavior detection - Incident documentation and analysis

Competitive Advantages

  • Contextual Intelligence: Understanding intent, not just presence
  • Customization: Natural language configuration vs. fixed algorithms
  • Cost Effectiveness: Commodity hardware with advanced AI
  • Flexibility: Prompt-based adaptation to specific use cases

Current Status

Research Phase: - Investigating Moondream deployment options - Hardware platform evaluation - Market validation through industry conversations

Next Steps: - Develop iOS MVP for initial testing - Validate technical approach with real-world scenarios - Assess market demand and pricing sensitivity

Resources & References

AI Models: - Moondream.ai - Vision language model - Moondream Station CLI - Local deployment

Hardware Considerations: - Mac Mini touchscreen capabilities - Linux GPU optimization options - Android embedded system examples

Technical Challenges

Current Limitations: - Moondream 3 not yet available on Mac/MLX - Power requirements for mobile applications (12V for forklifts) - Ruggedization needs for industrial environments - Real-time processing performance requirements

Research Areas: - Alternative vision models for better Mac compatibility - Embedded system optimization - Industrial-grade hardware integration - Network connectivity in factory environments