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AI Vision Detection (EO/IR)

DetectionOpen-Source Referenced

Passive electro-optical / infrared sensors paired with computer-vision classifiers that detect, track and classify drones from imagery without emitting any signal. Increasingly the backbone of modern C-UAS sensor fusion.

How It Works

Daylight EO and thermal IR cameras feed a CNN/transformer model trained on drone signatures. The model detects sub-pixel moving objects, classifies them by airframe, and cues kinetic or EW effectors. Operates entirely passively: no RF emissions to be geolocated by the adversary.

Technical Specifications

range
1–8 km depending on lens and weather
cost
$30,000–$300,000 per node
deployment Time
Hours (mast/vehicle), permanent (fixed)
crew Required
0–1 (semi-autonomous)
weight
5–40 kg per sensor head
power Requirement
100–400 W

Advantages

  • + Fully passive: undetectable by adversary RF intel
  • + Works against fiber-optic and autonomous drones (no RF needed)
  • + Day/night operation with cooled IR
  • + Scales horizontally with cheap sensor nodes

Disadvantages

  • − Degraded by fog, heavy rain, dust, low cloud
  • − Requires significant compute and trained models
  • − False positives on birds without good classifier
  • − Limited range vs. radar

Tactical Deployment Tips

  • ▸ Mesh multiple sensor nodes for 360° coverage
  • ▸ Cue kinetic effectors and EW from vision tracks to remain emissions-quiet
  • ▸ Train classifiers on local drone inventory for best accuracy

Limitations & Vulnerabilities

  • ⚠ Weather-dependent
  • ⚠ Line-of-sight only: terrain masks targets

Drones It Defeats

Drone types ranked by how well this system defeats them, tap any drone for detailsi

⚠ How Adversaries Defeat This System

Active enemy adaptations observed in the field, distinct from passive limitations above

  • ▸ Fog, rain, dust degrade EO/IR range significantly
  • ▸ Very small or low-contrast airframes evade classifier at range

Sources & Further Reading

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