INDAIR: Semi-autonomous fire detection in industrial halls

Client CountryCzech Republic

  • Client typeEnterprise
  • IndustryManufacturing
  • Application areasFacility & Asset Management, Legal, Compliance & Risk, Operations & Process Automation
  • AI technologiesAdvanced Analytics / Data Science, Computer vision and video analysis, Machine Learning, Optimization & Planning, Robotics & Control Systems
  • Business impactsEmployee Enablement & Productivity, Risk Reduction & Compliance
  • Data types3D Data, Geospatial Data, Image Data, Sensor / IoT Data, Video Data
  • Delivery modelsCustom Development, Product / Licensed Software, Service / Subscription
  • DeploymentsOn-premise
  • Key capabilitiesAnomaly, Risk & Fraud Detection, Planning, Scheduling & Optimization
  • Project stagesInitial Production Deployment
  • Solution formsIntegrated Edge / On-device Solution

Solution Description

Problem description

Industrial halls with robotics and heavy machinery face a high risk of fires. Traditional sensors can pick up a signal but cannot reliably confirm whether a fire is actually burning, leading to either false alarms or delayed response. Each unnecessary intervention costs money, slows production and puts employee safety at risk. Companies therefore need fast, accurate and safe signal verification before launching costly interventions.

Solution

Fly4Future has developed the INDAIR drone – a semi-autonomous system that, upon activation of a fire sensor, takes off independently, navigates safely using 3D lidar even without GNSS, and verifies the actual situation. The pilot only issues basic commands, while the drone itself evaluates whether the flight path is navigable and whether there is a real fire. Thanks to its protective cage, it is safe for indoor operation and can touch obstacles without being damaged. The result is faster, more reliable incident confirmation and minimized costs.

Main Users of the Solution

Shift Supervisor, Safety Technician, Integrated Rescue System (Firefighter)

Project timeframe (months)

6

Technologies used

Thermal imager/IR camera 3D lidar (multi-layer) RGB camera Visual SLAM (Simultaneous Localization and Mapping) Inertial unit (IMU: gyroscope, accelerometer, magnetometer) Autonomous obstacle avoidance Caged drone Fail-safe system Low-latency video streaming module (Wi-Fi/5G) Edge computing (mini GPU/TPU unit) Digital twin hall

Additional services

  • AI strategy and roadmap
  • Identification and prioritization of suitable use-cases
  • Data collection and pre-processing
  • Change management and user training
  • Compliance / regulatory support

Implementation

Project Owner on the Client's Side

Head of Innovation / Digital Transformation

Participation on the Client's Side

  • Project and change management
  • Quality, safety, compliance

Form of Supplier Involvement

Complete realization

Operation and Maintenance

Operational Model

Supplier, covered by a service agreement

Needed Competencies on the Client's Side

Training provided by the supplier.

Other Resources or Infrastructure

Charging stations, safety cabinets for batteries.

Impact and Results

Qualitative Benefits

Elimination of false emergency service dispatches. A requirement from the Integrated Rescue System for technology integration.

Quantitative Results

Up to a 20% annual reduction in costs for false Integrated Rescue System dispatches. A 50–70% reduction in the number of false alarms. Incident verification up to 10x faster than manual inspection.

Lessons Learned and Recommendations

Key Success Factors

Proprietary technology for autonomous control and flight in indoor environments without GNSS. Protective drone cage.

Biggest Challenges

The drone’s overall design must be both compact and safe. This challenge was overcome through team collaboration and by leveraging industry experts.

Recommendation for Others

Investing in autonomous technologies that ultimately save costs.

Promotion

Demo / Public Resources

  • NDA. The client reserves the rights to the project's public disclosures.

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