Abstract:
Object detection and classification has become the hall-mark of machine
learning and grabbed the attention of computer vision research community
for several years. However, detecting small objects in large images still
remains an active area of research. Detecting small objects is challenging
because of small subject area on image and few unique features of the
object. Moreover, due to limited annotated data availability, it is difficult
to employ machine learning algorithms. This is specially true for remote
sensing applications. Detecting cars in satellite imagery can help in traffic
monitoring, urban planning and surveillance. To tackle these challenges, we
employ data augmentation using image blending, and style blending using
Generative Adversial Networks (GAN) to augment subject top-view car
images in satellite data and create large datasets which otherwise may
require substantial human effort. The augmented dataset is utilized to train
standard object detection Convolutional Neural Networks (CNN) and
benchmark their performance on standard datasets.
