In the context of road perception for autonomous vehicles, short-wave infrared (SWIR) has opened up new perspectives beyond the visible spectrum, which is prone to performance degradation in harsh weather conditions like fog, rain and dust. This paper aims to analyze the feasibility of using SWIR images to enhance object detection and segmentation in such weather conditions. In our experiments, we used data obtained from three different cameras – including two SWIR technologies and a conventional visible camera – in different weather conditions. The conditions include a clear day for reference, rain at different rainfall rates, and fog at different visibility ranges. We explored the performance of several deep learning algorithms, originally trained on images from visible domain applied directly to SWIR images. Quantitative and qualitative analyses for detection and segmentation were conducted. When applied to SWIR modalities, the algorithms prove to perform comparatively to visible in the reference case and to improve detection and segmentation in harsh weather cases.