Confidence-Driven Region Mixing for Optical Remote Sensing Domain Adaptation Object Detection

Object detection is a challenging task that aims to locate and classify instances in an image simultaneously. High-performing deep detectors are typically trained on extensive labeled datasets and will face the problem of accuracy degradation in different scenarios. Through domain adaptation, the detector can attain generalization, making precise detections even in the presence of different data distributions without labels. In this article, we propose a confidence-driven region mixing (CR-Mixing) framework, which is a novel approach that integrates the region-selective mixing (RSM) method into a mean-teacher framework with an unbiased adversarial module (UAM), addressing the domain-shift and knowledge transfer problems in the unsupervised domain adaptation (UDA) object detection task. First, we construct RSM that utilizes pseudodetection labels to compute object information at the region level and integrates this information in a balanced manner to maximize accurate foreground details for the detection model. This enables the detection model to adjust its focus to unlabeled domains gradually. Second, to acquire a backbone network with object-focused features, we develop the UAM method. This method is a multiscale alignment network designed to constrain the backbone network in learning domain-invariant features. We conducted extensive experiments on two benchmarks, and the final results show that the CR-Mixing model demonstrates significant effectiveness in robust UDA for optical remote sensing object detection tasks.