Unsupervised domain adaptation for multispectral object detection

Abstract

Recent advancements in image sensor technology and deep learning have led to increased expectations for improving object detection performance in EO/IR systems. However, the large-scale datasets made available for deep learning model training exhibit significant differences in data distribution compared to the multispectral images acquired from the actual target domain, EO/IR systems. Because of this, when a pretrained deep-learning model is applied to real-world issues for multispectral object detection, it does not work well for inputs with a different distribution from a training dataset. Therefore, a dataset for additional fine-tuning is required. However, labeled datasets required for retraining can be challenging to acquire in practice because of security issues and high acquisition costs. These domain differences and dataset issues are crucial challenges concerning the use of AI in military applications. We herein propose an unsupervised domain adaptation method for object detection in multispectral images with domain discrepancies with a pretrained model on a large public dataset. We train an encoder network without direct labeling to generate feature maps similar to the backbone applied to existing detectors. The proposed approach has the advantage of being able to directly utilize pretrained object detection models from large-scale public datasets by simply training the encoder network alone.