Our new X account is live! Follow @wizwand_team for updates
WorkDL logo mark

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

TaskDatasetResultRank
Dense Anomaly DetectionSMIYC AnomalyTrack
AP82.9
30
Dense Anomaly DetectionSMIYC ObstacleTrack
AP54.1
21
Obstacle SegmentationSMIYC Obstacle Track
AP54.1
11
Dense Out-of-Distribution DetectionSegmentMeIfYouCan (SMIYC) LAF-noKnown
AP82.9
10
Showing 4 of 4 rows

Other info

Follow for update