Road Anomaly Segmentation Based on Pixel-Wise Logit Variance with Iterative Background Highlighting

Anomaly segmentation on the urban landscape scene is an important task in autonomous driving. This process exploits a pre-trained semantic segmentation network to estimate anomalous regions. Anomaly segmentation approaches implemented with extra requirements such as out-of-domain data, extra network, or network retraining might increase the computational cost or degradation of segmentation performance. In this study, to exploit information from the segmentation network for more robust anomaly segmentation, we propose the use of pixel-wise logit variance, which tends to be small for anomalies as network outputs even logits without confidence. Additionally iterative background highlighting is proposed to robustly detect anomalous objects on the background, which is implemented by feeding the logits back into the linear classifier of the network. We achieved state-of-the-art performance among anomaly segmentation approaches without extra requirements, reaching relative average precision improvements of 21.7% on the Fishyscapes Lost&Found and 17.4% on the Fishyscapes Static compared to the state-of-the-art method. The code of this work is available at our Github repository (https://github.com/hagg30/LogitVar).