Detecting Road Obstacles by Erasing Them

12/25/2020
by   Krzysztof Lis, et al.
13

Vehicles can encounter a myriad of obstacles on the road, and it is not feasible to record them all beforehand to train a detector. Our method selects image patches and inpaints them with the surrounding road texture, which tends to remove obstacles from those patches. It them uses a network trained to recognize discrepancies between the original patch and the inpainted one, which signals an erased obstacle. We also contribute a new dataset for monocular road obstacle detection, and show that our approach outperforms the state-of-the-art methods on both our new dataset and the standard Fishyscapes Lost Found benchmark.

READ FULL TEXT

page 1

page 3

page 4

page 6

page 11

page 14

page 15

page 16

research
10/04/2022

Perspective Aware Road Obstacle Detection

While road obstacle detection techniques have become increasingly effect...
research
09/15/2016

Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles

Detecting small obstacles on the road ahead is a critical part of the dr...
research
03/12/2020

LiDAR guided Small obstacle Segmentation

Detecting small obstacles on the road is critical for autonomous driving...
research
04/23/2019

A Novel Multi-layer Framework for Tiny Obstacle Discovery

For tiny obstacle discovery in a monocular image, edge is a fundamental ...
research
02/23/2015

Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding

Classifying single image patches is important in many different applicat...
research
12/20/2016

Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling

The detection of small road hazards, such as lost cargo, is a vital capa...
research
11/17/2021

Tiny Obstacle Discovery by Occlusion-Aware Multilayer Regression

Edges are the fundamental visual element for discovering tiny obstacles ...

Please sign up or login with your details

Forgot password? Click here to reset