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AI startup Flox Intelligence aims to reduce wildlife strikes on infrastructure with smart deterrent system

Swedish company uses on-device AI to identify animals and emit species-specific sounds, varying responses to prevent habituation

By LineZotpaper
Published
Read Time2 min
Sources3 outlets
Flox Intelligence, a startup spun out of KTH Royal Institute of Technology in Stockholm, is developing AI-powered edge devices to deter wildlife from train tracks, airports, and roads. The company, a finalist at TechCrunch Disrupt's Startup Battlefield 200 pitch competition, aims to reduce the billions in damages and delays caused by animal strikes.

Wildlife collisions with infrastructure are a costly problem worldwide. The FAA estimates that wildlife strikes have cost U.S. civil aviation about $272 million a year on average between 1990 and 2025. In France, rail operator SNCF says wandering animals affect more than 8,000 trains each year, ranking among its five leading causes of delays. In Brittany alone, 120 trains hit animals in 2023, disrupting 660 services and causing over 15,000 minutes of delays.

Sara Nozkova, who spent 13 years working in transportation and autonomous vehicles, founded Flox Intelligence after witnessing the damage caused by such incidents. "I was convinced the answer was not a louder deterrent," she told TechCrunch. She joined the AI lab at KTH Royal Institute of Technology to develop a system that adapts its response based on animal behavior and species.

Flox makes edge devices that process data on-site. A motion sensor triggers a camera, which uses on-device models to identify nearby animals. The device then emits a species-appropriate sound to scare them away. Nozkova says the system learns from experience and varies its responses so animals do not become habituated. The company currently sells battery-operated devices.

Traditional deterrent methods include sirens, lights, fences, falcons, and culling. Flox's approach aims to be more targeted and adaptive. The startup spun out of KTH in 2020 and is participating in Startup Battlefield 200 at TechCrunch Disrupt, taking place October 13-15 at Moscone West in San Francisco.

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Analysis

Why This Matters

  • Wildlife strikes cause substantial economic damage and safety risks for aviation, rail, and road transport, as well as harm to animals.
  • A non-lethal, adaptable AI solution could reduce costs and disruptions for infrastructure operators while improving animal welfare.
  • If successful, Flox's approach could be deployed globally, offering a scalable alternative to traditional deterrents.

Background

Wildlife collisions with transportation infrastructure are a persistent problem. Airports and railways have long used fences, sirens, lights, birds of prey, and culling to keep animals clear. However, many methods are costly, require constant maintenance, or lose effectiveness as animals habituate to repeated stimuli. Flox attempts to solve habituation by varying its audio responses based on species and observed behavior.

Key Perspectives

[Infrastructure operators]: For rail and airport authorities, reducing wildlife strikes means fewer delays, lower repair costs, and improved safety. A device that operates autonomously and adapts over time could offer operational savings. [Wildlife advocates]: Flox's non-lethal, species-specific approach is likely more humane than culling or falconry. However, the long-term effectiveness and ecological impact of repeated audio deterrents remain to be seen. [Critics/Skeptics]: Questions remain about whether the system can truly prevent habituation over long periods, and how well its on-device models will perform across diverse environments and species not represented in training data. The battery-operated devices may also face maintenance challenges in remote locations.

What to Watch

  • Initial pilot deployments with transport authorities and reported strike reduction rates.
  • Expansion of the device's species library and model accuracy in new regions.
  • Partnerships with aviation authorities or rail operators for large-scale testing.

Sources

Zotpaper

Written by software from the reporting listed above, scored by an automated standards desk, and published without a person reading it first. If something here is wrong, tell the editor and it will be put right.

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