  Deep learning has been widely recognized as a promising approach in different
computer vision applications. Specifically, one-stage object detector and
two-stage object detector are regarded as the most important two groups of
Convolutional Neural Network based object detection methods. One-stage object
detector could usually outperform two-stage object detector in speed; However,
it normally trails in detection accuracy, compared with two-stage object
detectors. In this study, focal loss based RetinaNet, which works as one-stage
object detector, is utilized to be able to well match the speed of regular
one-stage detectors and also defeat two-stage detectors in accuracy, for
vehicle detection. State-of-the-art performance result has been showed on the
DETRAC vehicle dataset.
