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<p>Photogrammetric products obtained by processing data acquired with Unmanned Aerial Vehicles (UAVs) are used in many fields. Various structures are analysed, including roads. Many roads located in cities are characterised by heavy traffic. This makes it impossible to avoid the presence of cars in aerial photographs. However, they are not an integral part of the landscape, so their presence in the generated photogrammetric products is unnecessary. The occurrence of cars in the images may also lead to errors such as irregularities in digital elevation models (DEMs) in roadway areas and the blurring effect on orthophotomaps. The research aimed to improve the quality of photogrammetric products obtained with the Structure from Motion algorithm. To fulfil this objective, the Yolo v3 algorithm was used to automatically detect cars in the images. Neural network learning was performed using data from a different flight to ensure that the obtained detector could also be used in independent projects. The photogrammetric process was then carried out in two scenarios: with and without masks. The obtained results show that the automatic masking of cars in images is fast and allows for a significant increase in the quality of photogrammetric products such as DEMs and orthophotomaps.</p>
</abstract>

Photogrammetric products obtained by processing data acquired with Unmanned Aerial Vehicles (UAVs) are used in many fields. Various structures are analysed, including roads. Many roads located in cities are characterised by heavy traffic. This makes it impossible to avoid the presence of cars in aerial photographs. However, they are not an integral part of the landscape, so their presence in the generated photogrammetric products is unnecessary. The occurrence of cars in the images may also lead to errors such as irregularities in digital elevation models (DEMs) in roadway areas and the blurring effect on orthophotomaps. The research aimed to improve the quality of photogrammetric products obtained with the Structure from Motion algorithm. To fulfil this objective, the Yolo v3 algorithm was used to automatically detect cars in the images. Neural network learning was performed using data from a different flight to ensure that the obtained detector could also be used in independent projects. The photogrammetric process was then carried out in two scenarios: with and without masks. The obtained results show that the automatic masking of cars in images is fast and allows for a significant increase in the quality of photogrammetric products such as DEMs and orthophotomaps.

Vehicle detection and masking in UAV images using YOLO to improve photogrammetric products

Department of Photogrammetry, Remote Sensing of Environment, and Spatial Engineering, Faculty of Geo-Data Science

Geodesy, and Environmental Engineering, AGH University of Science and Technology

Reports on Geodesy and Geoinformatics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Photogrammetric products obtained by processing data acquired with Unmanned Aerial Vehicles (UAVs) are used in many fields. Various structures are analysed,...

Vehicle detection and masking in UAV images using YOLO to improve photogrammetric products

Data publikacji: 24 gru 2022

Zakres stron: 15 - 23

Otrzymano: 08 lip 2022

Przyjęty: 01 gru 2022

DOI: https://doi.org/10.2478/rgg-2022-0006

Słowa kluczowe
deep learning, photogrammetry, Structure from motion, roads, object detection

© 2022 Karolina Pargieła, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Vehicle detection and masking in UAV images using YOLO to improve photogrammetric products

Data publikacji: 24 gru 2022

Zakres stron: 15 - 23

Otrzymano: 08 lip 2022

Przyjęty: 01 gru 2022

DOI: https://doi.org/10.2478/rgg-2022-0006

Słowa kluczowedeep learning, photogrammetry, Structure from motion, roads, object detection

© 2022 Karolina Pargieła, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
deep learning, photogrammetry, Structure from motion, roads, object detection