@article{MAKHILLJEAS2020152119476,
    title = {Detecting Vehicles using YOLO from Aerial Images},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {15},
    number = {21},
    pages = {3586-3592},
    year = {2020},
    issn = {1816-949x},
    doi = {jeasci.2020.3586.3592},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2020.3586.3592},
    author = {Shighaf,Omar and},
    keywords = {Deep learning,convolutional neural networks,YOLO,COWC,VEDAI,OIRDS},
    abstract = {Detection of vehicles from aerial images is a
challenging subject due to the large image resolution with
small targets and variant orientations. Unfortunately, there
isn’t any dataset large enough to be suitable for training
deep models. Therefore, we recognize COWC, large
aerial image dataset to use in vehicle detection. In this
project, the third version of popular YOLO is modified to
vastly improve its performance on aerial data. We trained
on a large amount of aerial images from COWC dataset.
The proposed detector was able to achieve mAP = 95%
on VEDAI dataset. It outperformed SSD and R-CNN. For
the OIRDS dataset, we achieved mAP = 67% without any
previous training.}
    }