Domain Adaptive Object Detection for Space Applications with Real-Time Constraints

Abstract

Object detection is essential in space applications targeting Space Domain Awareness and also applications involving relative navigation scenarios. Current deep learning models for Object Detection in space applications are often trained on synthetic data from simulators, however, the model performance drops significantly on real-world data due to the domain gap. However, domain adaptive object detection is an overlooked problem in the community. In this work, we first show the importance of domain adaptation and then explore Supervised Domain Adaptation (SDA) to reduce this gap using minimal labeled real data. We build on a recent semi-supervised adaptation method and tailor it for object detection. Our approach combines domain-invariant feature learning with a CNN-based domain discriminator and invariant risk minimization using a domain-independent regression head. To meet real-time deployment needs, we test our method on a lightweight Single Shot Multibox Detector (SSD) with MobileNet backbone and on the more advanced Fully Convolutional One-Stage object detector (FCOS) with ResNet-50 backbone. We evaluated on two space datasets, SPEED+ and SPARK. The results show up to 20-point improvements in average precision (AP) with just 250 labeled real images.