GISD: generation of corner cases in infrared autonomous driving dataset with stable diffusion

Situational awareness is vital for safe autonomous driving. With the recent developments in deep neural networks, detection of vehicles, pedestrians, and traffic signs become popular topics with high performance, but the detection of an unusual object that has not been encountered before in the scene also called a corner case, is not studied well. Although there are some studies on corner case detection in the visible domain, detectors developed for the visible domain are susceptible to light and weather conditions. Therefore, models might hardly detect corner cases that occur in poor lighting conditions, which can also happen in the real world, and put lives at risk by failing early detection. On the other hand, infrared cameras provide high performance in poor light and foggy weather conditions. However, corner cases in infrared images are not included in the datasets and this issue has not been studied before. Therefore, in this paper, we introduce a synthetically generated high-quality infrared dataset with stable diffusion for corner case detection in infrared images. This dataset addresses situations that may cause a hazard to autonomous vehicles in poor visibility by generating these situations in the infrared domain. As another contribution of this study, we present a detection model trained with the corner cases in infrared images and establish a baseline performance for the model. We believe this work will create a foundation for studies on corner cases in infrared images.