TY  - JOUR
T1  - Detecting Vehicles using YOLO from Aerial Images
AU - Abdallah, Shighaf AU - Hamdoun, Omar AU - Jafar, Assef 
JO  - Journal of Engineering and Applied Sciences
VL  - 15
IS  - 21
SP  - 3586
EP  - 3592
PY  - 2020
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2020.3586.3592
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2020.3586.3592
KW  - Deep learning
KW  -convolutional neural networks
KW  -YOLO
KW  -COWC
KW  -VEDAI
KW  -OIRDS
AB  - 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.
ER  -