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

AUTORZY: KAROLINA PARGIEŁA

ABSTRAKT: 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 itimpossible to avoid the presence of cars in aerial photographs. However, they are not an integral part of the landscape, so theirpresence in the generated photogrammetric products is unnecessary. The occurrence of cars in the images may also lead to errorssuch as irregularities in digital elevation models (DEMs) in roadway areas and the blurring effect on orthophotomaps. Theresearch aimed to improve the quality of photogrammetric products obtained with the Structure from Motion algorithm. To fulfilthis objective, the Yolo v3 algorithm was used to automatically detect cars in the images. Neural network learning was performedusing data from a different flight to ensure that the obtained detector could also be used in independent projects. Thephotogrammetric process was then carried out in two scenarios: with and without masks. The obtained results show that theautomatic masking of cars in images is fast and allows for a significant increase in the quality of photogrammetric products suchas DEMs and orthophotomaps.

PEŁNY TEKST POD NUMEREM DOI: doi.org/10.2478/rgg-2022-0006