Unsupervised object detection via LWIR/RGB translation

In this work, we present two new methods to overcome the lack of annotated long-wavelength infrared (LWIR) databy exploiting the abundance of similar RGB imagery. We introduce a novel unsupervised adaptation to the cycle GANarchitecture for translating non-corresponding LWIR/RGBdatasets. Our ultimate goal is high detection rates in the synthetic RGB/real LWIR imagery using only RGB labelled imagery for training detection algorithms. We translate LWIR imagery to RGB, allowing us to use an RGB trained detection algorithm. We, thereby remove the need for labelled LWIR imagery for training detection algorithms. In addition, we translate RGB to LWIR to fine-tune a network for detection in real LWIR imagery. Experimental results show that our adaption helps to create synthetic RGB imagery with higher detection rates across two different datasets. We also find that combining the synthetic RGB and real LWIR imagery produces higher F1 scores on the RGB trained detection network. Fine-tuning detection networks with synthetic LWIR and testing with real LWIR imagery produce the highest F1 scores.