The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIV-2/W1-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-2/W1-2021, 125–130, 2021
https://doi.org/10.5194/isprs-archives-XLIV-2-W1-2021-125-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-2/W1-2021, 125–130, 2021
https://doi.org/10.5194/isprs-archives-XLIV-2-W1-2021-125-2021

  15 Apr 2021

15 Apr 2021

DEEP LEARNING FOR CODED TARGET DETECTION

V. V. Kniaz1,2, L. Grodzitskiy1, and V. A. Knyaz1,2 V. V. Kniaz et al.
  • 1State Res. Institute of Aviation Systems (GosNIIAS), 125319, 7, Victorenko str., Moscow, Russia
  • 2Moscow Institute of Physics and Technology (MIPT), Russia

Keywords: photogrammetry, deep learning, coded target detection

Abstract. Coded targets are physical optical markers that can be easily identified in an image. Their detection is a critical step in the process of camera calibration. A wide range of coded targets was developed to date. The targets differ in their decoding algorithms. The main limitation of the existing methods is low robustness to new backgrounds and illumination conditions. Modern deep learning recognition-based algorithms demonstrate exciting progress in object detection performance in low-light conditions or new environments. This paper is focused on the development of a new deep convolutional network for automatic detection and recognition of the coded targets and sub-pixel estimation of their centers.