Classification and object detection with image assisted total station and machine learning

Abstract This paper deals with applications of digital imaging total stations in a geodetic context using artificial intelligence (AI). We present two different use cases. The first is to minimise manual intervention by the operator by classifying images with different backgrounds. We use a developed software to control a total station extended by an industrial camera, which is used for the in-situ calibration of the camera. We show that the AI successfully tests the captured image for its suitability for further use and under which circumstances the AI fails. The second case is the detection of different geodetic targets (reflective and non-reflective). Captured images of an imaging total station are automatically checked to see whether a supposed target is shown in the image, identify it and localise it in the image. Already implemented applications for target identification are to be supported in this way and extended by further information.