Tiny Obstacle Discovery by Occlusion-Aware Multilayer Regression

TL;DR

This paper introduces an occlusion-aware multilayer regression method that improves tiny obstacle detection from monocular images by leveraging edge cues and occlusion information, addressing challenges posed by small size and appearance similarity.

Contribution

It proposes a novel multilayer regression framework that incorporates occlusion awareness to enhance tiny obstacle detection accuracy.

Findings

Achieves higher detection accuracy for tiny obstacles compared to existing methods.

Effectively handles occlusion and appearance challenges in monocular images.

Demonstrates robustness across various obstacle types and environments.

Abstract

Edges are the fundamental visual element for discovering tiny obstacles using a monocular camera. Nevertheless, tiny obstacles often have weak and inconsistent edge cues due to various properties such as small size and similar appearance to the free space, making it hard to capture them. ...