Spatial Alignment for Unsupervised Domain Adaptive Single-Stage Object Detection

Abstract

Domain adaptation methods are proposed to improve the performance of object detection in new domains without additional annotation costs. Recently, domain adaptation methods based on adversarial learning to align source and target domain image distributions are effective. However, for object detection tasks, image-level alignment enforces the alignment of non-transferable background regions, which affects the performance of important target regions. Therefore, how to balance the alignment of background and target remains a challenge. In addition, the current research with good effect is based on two-stage detectors, and there are relatively few studies on single-stage detectors. To address these issues, in this paper, we propose a selective domain adaptation framework for the spatial alignment of a single-stage detector. The framework can identify the background and target and pay different attention to them. On the premise that the single-stage detector does not generate region suggestions, it can achieve domain feature alignment and reduce the influence of the background, enabling transfer between different domains. We validate the effectiveness of our method for weather discrepancy, camera angles, synthetic to real-world, and real images to artistic images. Extensive experiments on four representative adaptation tasks show that the method effectively improves the performance of single-stage object detectors in different domains while maintaining good scalability.

Keywords

• domain adaptation
• object detection
• transfer learning