  The area of domain adaptation has been instrumental in addressing the domain
shift problem encountered by many applications. This problem arises due to the
difference between the distributions of source data used for training in
comparison with target data used during realistic testing scenarios. In this
paper, we introduce a novel MultiScale Domain Adaptive YOLO (MS-DAYOLO)
framework that employs multiple domain adaptation paths and corresponding
domain classifiers at different scales of the recently introduced YOLOv4 object
detector to generate domain-invariant features. We train and test our proposed
method using popular datasets. Our experiments show significant improvements in
object detection performance when training YOLOv4 using the proposed MS-DAYOLO
and when tested on target data representing challenging weather conditions for
autonomous driving applications.
