  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. Building on our baseline multiscale DAYOLO framework, we introduce
three novel deep learning architectures for a Domain Adaptation Network (DAN)
that generates domain-invariant features. In particular, we propose a
Progressive Feature Reduction (PFR), a Unified Classifier (UC), and an
Integrated architecture. We train and test our proposed DAN architectures in
conjunction with YOLOv4 using popular datasets. Our experiments show
significant improvements in object detection performance when training YOLOv4
using the proposed MS-DAYOLO architectures and when tested on target data for
autonomous driving applications. Moreover, MS-DAYOLO framework achieves an
order of magnitude real-time speed improvement relative to Faster R-CNN
solutions while providing comparable object detection performance.
