Detecting Road Obstacles by Erasing Them
About
Vehicles can encounter a myriad of obstacles on the road, and it is impossible to record them all beforehand to train a detector. Instead, we select image patches and inpaint them with the surrounding road texture, which tends to remove obstacles from those patches. We then use a network trained to recognize discrepancies between the original patch and the inpainted one, which signals an erased obstacle.
Krzysztof Lis, Sina Honari, Pascal Fua, Mathieu Salzmann• 2020
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Dense Anomaly Detection | SMIYC AnomalyTrack | AP82.9 | 30 | |
| Dense Anomaly Detection | SMIYC ObstacleTrack | AP54.1 | 21 | |
| Obstacle Segmentation | SMIYC Obstacle Track | AP54.1 | 11 | |
| Dense Out-of-Distribution Detection | SegmentMeIfYouCan (SMIYC) LAF-noKnown | AP82.9 | 10 |
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