Object detection in the thermal infrared spectral range by diffusion model based domain adaptation of the training data

This work examines the extent to which training data can be artificially generated for a target domain in an unsupervised manner to train an object detector in the target domain in the presence of little or no real training data. If the distributions of a source and target domain differ, but the same task is performed on both, this is referred to as domain adaptation. In the field of image processing, generative approaches are often used when attempting to transform the distribution of the source domain into the target domain. In this work, a generative method, a Denoising Diffusion Probabilistic Model, is investigated for the domain adaptation from the visible spectrum (VIS) to the thermal infrared (IR). Systematic extensions, such as the use of alternative noise schedules, were incorporated and evaluated. The partial results of the domain adaptation are significantly improved by the implemented extensions. In a subsequent step, a thermal infrared object detector is trained using the results of the domain adaptation. The publicly available Multi-scenario Multi-Modality Benchmark to Fuse Infrared and the recording vehicle MODISSA are used here for evaluation.