PanopticVis: Integrated Panoptic Segmentation for Visibility Estimation at Twilight and Night

Abstract

Visibility affects traffic flow and control on city roads, highways, and runways. Visibility distance or level is an important measure for predicting the risk on the road. Particularly, it is known that traffic accidents can be raised at foggy twilight and night. Cameras monitor visual conditions like fog. However, only a few papers have tackled such nighttime vision with visibility estimation. This paper proposes a Panoptic Segmentation-based foggy night visibility estimation integrating multiple Deep Learning models: DeepReject/Depth/ Scene/Vis/Fog using single images. We call PanopticVis. DeepFog is trained for no-fog and heavy fog. DeepVis for medium fog is trained by annotated visibility physical scales in a regression manner. DeepDepth is improved to be robust to strong local illumination. DeepScene panoptic-segments scenes with stuff and things, booted by Deep-Depth. DeepReject conducts adversarial visual conditions: strong illumination and darkness. Notably, the proposed multiple Deep Learning framework provides high efficiency in memory, cost, and easy-tomaintenance. Unlike previous synthetic test images, experimental results show the effectiveness of the proposed integrated multiple Deep Learning approaches for estimating visibility distances on real foggy night roads. The superiority of PanopticVis is demonstrated over state-of-the-art panoptic-based Deep Learning models in terms of stability, robustness, and accuracy.