DEEP LEARNING FOR CODED TARGET DETECTION
| Autor: | V. V. Kniaz, L. Grodzitskiy, V. A. Knyaz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIV-2-W1-2021, Pp 125-130 (2021) |
| Druh dokumentu: | article |
| ISSN: | 1682-1750 2194-9034 |
| DOI: | 10.5194/isprs-archives-XLIV-2-W1-2021-125-2021 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|