  Infrared imagery can help in low-visibility situations such as fog and
low-light scenarios, but it is prone to thermal noise and requires further
processing and correction. This work studies the effect of different infrared
processing pipelines on the performance of a pedestrian detection in an urban
environment, similar to autonomous driving scenarios. Detection on infrared
images is shown to outperform that on visible images, but the infrared
correction pipeline is crucial since the models cannot extract information from
raw infrared images. Two thermal correction pipelines are studied, the shutter
and the shutterless pipes. Experiments show that some correction algorithms
like spatial denoising are detrimental to performance even if they increase
visual quality for a human observer. Other algorithms like destriping and, to a
lesser extent, temporal denoising, increase computational time, but have some
role to play in increasing detection accuracy. As it stands, the optimal
trade-off for speed and accuracy is simply to use the shutterless pipe with a
tonemapping algorithm only, for autonomous driving applications within varied
environments.
