{"id":3004,"date":"2026-07-21T14:18:53","date_gmt":"2026-07-21T06:18:53","guid":{"rendered":"https:\/\/midradar.com\/?post_type=news&p=3004"},"modified":"2026-07-22T10:30:47","modified_gmt":"2026-07-22T02:30:47","slug":"how-to-calculate-eo-ir-detection-recognition-and-identification-range","status":"publish","type":"news","link":"https:\/\/midradar.com\/de\/news\/how-to-calculate-eo-ir-detection-recognition-and-identification-range\/","title":{"rendered":"How to Calculate EO\/IR Detection, Recognition and Identification Range"},"content":{"rendered":"

Long-range camera<\/a> datasheets often present one headline distance: detection up to a certain number of kilometers, recognition at a shorter distance and identification at another. Those figures look easy to compare, but they can be misleading unless every supplier uses the same target, atmosphere, probability criterion and image-quality model.<\/p>\n

EO\/IR range is not a property of the lens alone. It is the outcome of an imaging chain that includes target size, focal length, detector pitch, sensor resolution, optical transmission, atmospheric attenuation, target-to-background contrast, stabilization, image processing, display conditions and the observer or classification algorithm.<\/p>\n

This guide provides a transparent way to screen camera configurations and, more importantly, explains what must be verified during procurement and acceptance testing.<\/p>\n

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How to Calculate EO\/IR Detection, Recognition and Identification Range<\/p><\/div>\n

 <\/p>\n

1. Detection, Recognition and Identification Are Different Tasks<\/b><\/strong><\/h2>\n\n\n\n\n\n\n
DRI level<\/strong><\/td>\nOperational question<\/strong><\/td>\nExample<\/strong><\/td>\n<\/tr>\n
Detektion<\/td>\nIs a relevant object present?<\/td>\nA warm object is visible against the background.<\/td>\n<\/tr>\n
Erkennung<\/td>\nWhat general class is it?<\/td>\nThe operator can distinguish a person from a vehicle.<\/td>\n<\/tr>\n
Identifikation<\/td>\nWhich member or subtype is it?<\/td>\nThe operator can identify a pickup truck rather than a generic vehicle, subject to the agreed task definition.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

These definitions must be written into the requirement. \u201cIdentification\u201d can mean different things to a border operator, port authority, airport or industrial security team. Facial identification, vehicle-type classification and confirmation that a target is carrying an object require very different image detail.<\/p>\n

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How to Calculate EO\/IR Detection, Recognition and Identification Range<\/p><\/div>\n

2. Start with Angular Sampling: IFOV<\/b><\/strong><\/h2>\n

The instantaneous field of view (IFOV) is the angle represented by one detector pixel. Under the small-angle approximation:<\/p>\n\n\n\n
IFOV (radians per pixel) \u2248 Pixel Pitch \u00f7 Focal Length<\/strong>
\n<\/strong>Ground Sample Size per Pixel \u2248 Distance \u00d7 IFOV<\/strong>
\n<\/strong>Pixels on Target \u2248 Target Dimension \u00f7 (Distance \u00d7 IFOV)<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

All dimensions must use consistent units. For example, a 12 \u00b5m pixel pitch is 12 \u00d7 10\u207b\u2076 m. A 300 mm focal length is 0.300 m.<\/p>\n

This calculation estimates geometric sampling. It does not yet include blur, optical modulation transfer, atmosphere, noise, contrast, stabilization or image processing.<\/p>\n

 <\/p>\n

3. Worked First-Order Example<\/b><\/strong><\/h2>\n

Assume an uncooled thermal camera with:<\/p>\n