Source-Free Domain Adaptive Detection of Concealed Objects in Passive Millimeter-Wave Images

Passive millimeter-wave (PMMW) imager can detect concealed objects under clothing in a touch-free manner. Existing detectors exhibit promising performance on specific PMMW datasets. However, these detectors will suffer severe degradation due to the inevitable domain shift when the environment changes in real applications. To avoid huge manual efforts in preparing the target domain dataset, unsupervised domain adaption (UDA) is proposed to tackle this issue. UDA holds the assumption that the labeled source data and unlabeled target data are accessible during the adaptation, but it is not always true due to the privacy concern. In this article, we aim to address this challenge by source-free domain adaption (SFDA), which migrates the source-trained model to target domain without the source data. Our method is built on the student–teacher framework and combines the merits of pseudo-label self-training and adversarial learning (AL). We also propose a novel spatio-temporal weighting strategy (STW). It mines the spatial self-correlation in single PMMW image and temporal cross correlation among PMMW images for teacher to produce high-quality pseudo-labels. It guides the student to select strong positive pairs for contrastive learning. In addition, the AL is further employed for feature alignment between teacher and student at image and instance levels, which ensures the student to learn domain-invariant features. Extensive experiments show that our method achieves the state-of-the-art performance. We further demonstrate that the proposed method can work in online settings. To the best of authors’ knowledge, this is the first work to incorporate SFDA for concealed object detection in PMMW images.