Aerial Multi-Vehicle Detection Dataset

Description

Aerial Multi-Vehicle Detection Dataset: Efficient road traffic monitoring is playing a fundamental role in successfully resolving traffic congestion in cities. Unmanned Aerial Vehicles (UAVs) or drones equipped with cameras are an attractive proposition to provide flexible and infrastructure-free traffic monitoring. Due to the affordability of  such drones, computer vision solutions for traffic monitoring have been widely used. Therefore, this dataset provide images that can be used for either training or evaluating Traffic Monitoring applications. More specifically, it can be used for training an aerial vehicle detection algorithm, benchmark an already trained vehicle detection algorithm, enhance an existing dataset and aid in traffic monitoring and analysis of road segments.  The dataset construction involved manually collecting all aerial images of vehicles using UAV drones and manually annotated into three classes 'Car', 'Bus', and ''Truck'.The aerial images were collected through manual flights in road segments in Nicosia or Limassol, Cyprus, during busy hours. The images are in High Quality, Full HD (1080p) to 4k (2160p) but are usually resized before training. All images were manually annotated and inspected afterward with the vehicles that indicate 'Car' for small to medium sized vehicles, 'Bus' for busses, and 'Truck' for large sized vehicles and trucks. All annotations were converted into VOC and COCO formats for training in numerous frameworks. The data collection took part in different periods, covering busy road segments in the cities of Nicosia and Limassol in Cyprus. The altitude of the flights varies between 150 to 250 meters high, with a top view perspective. Some of the images found in this dataset are taken from Harpy Data dataset [1]  The dataset includes a total of 9048 images of which 904 are split for validation, 905 for testing, and the rest 7239 for training.  Subset Images Car Bus Truck Training 7239 200301 1601 6247 Validation 904 23397  193  727 Testing 905 24715 208 770 It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).   [1] Makrigiorgis, R., 2021. Harpy Data Dataset. [online] Kios.ucy.ac.cy. Available at: [Accessed 22 September 2022].   **NOTE** If you use this dataset in your research/publication please cite us using the following : Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2022). Aerial Multi-Vehicle Detection Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7053442

Resources

Name Format Description Link
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7053442
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7053442
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7053442
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7053442
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7053442
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-7053442

Tags

  • object-detection
  • uav
  • yolo
  • voc
  • aerial
  • unmanned-aerial-vehicles
  • deep-learning
  • dataset
  • computer-vision
  • coco
  • traffic-monitoring
  • vehicles
  • training
  • vehicular

Topics

Categories