ICLD: An Instance Contrastive Learning Domain Adaptive SAR Object Detection Network

The feature alignment methods currently employed in domain adaptation for enhancing model transfer ability tend to compromise the discriminative capability of the model towards target features from different categories, consequently leading to a degradation in detector performance. To address this issue, this paper proposes a domain adaptation method for SAR (Synthetic Aperture Radar) image object detection based on instance contrastive learning. By utilizing a dual-branch prediction network, foreground and background instances are more accurately selected. Through instance contrastive learning, the proposed method narrows the distance between target features of the same category across different domains while widening the distance between target features of different categories. This approach facilitates alignment of the distributions of target features of the same category while preserving the discriminative nature of target features across different categories. Experimental results demonstrate that the proposed model achieves respective improvements in the Average Precision (AP) metric on two domain adaptation object detection datasets from optical images to SAR remote sensing images compared to SOTA methods.