Papers › Pixel-wise Anomaly Detection in Complex Driving Scenes
Pixel-wise Anomaly Detection in Complex Driving Scenes
Giancarlo Di Biase, Hermann Blum, Roland Siegwart, Cesar Cadena
The inability of state-of-the-art semantic segmentation methods to detect anomaly instances hinders them from being deployed in safety-critical and complex applications, such as autonomous driving. Recent approaches have focused on either leveraging segmentation uncertainty to identify anomalous areas or re-synthesizing the image from the semantic label map to find dissimilarities with the input image. In this work, we demonstrate that these two methodologies contain complementary information and can be combined to produce robust predictions for anomaly segmentation. We present a pixel-wise anomaly detection framework that uses uncertainty maps to improve over existing re-synthesis methods in finding dissimilarities between the input and generated images. Our approach works as a general framework around already trained segmentation networks, which ensures anomaly detection without compromising segmentation accuracy, while significantly outperforming all similar methods. Top-2 performance across a range of different anomaly datasets shows the robustness of our approach to handling different anomaly instances.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | Fishyscapes | Synboost | AP | 72.59 | #4 of 8 | Archive leaderboard | report |
| Anomaly Detection | Fishyscapes | Synboost | FPR95 | 18.75 | #4 of 8 | Archive leaderboard | report |
| Anomaly Detection | Fishyscapes L&F | SynBoost | AP | 43.22 | #9 of 18 | Archive leaderboard | report |
| Anomaly Detection | Fishyscapes L&F | SynBoost | FPR95 | 15.79 | #9 of 18 | Archive leaderboard | report |
| Anomaly Detection | Lost and Found | SynBoost | AP | 70.43 | #3 of 4 | Archive leaderboard | report |
| Anomaly Detection | Lost and Found | SynBoost | FPR | 4.89 | #3 of 4 | Archive leaderboard | report |
| Anomaly Detection | Road Anomaly | Synboost | AP | 41.83 | #8 of 10 | Archive leaderboard | report |
| Anomaly Detection | Road Anomaly | Synboost | FPR95 | 59.72 | #8 of 10 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes val | SynBoost | mIoU | 83.5 | #25 of 99 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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