鉄道軌道上の障害物検知における局所情報と大域情報
Local and Global Information in Obstacle Detection on Railway Tracks
通常の鉄道画像から浅いネットワークで線路領域をセグメンテーションし、障害物のない画像を生成する学習を通じて大域情報を制御的に取り込むことで、障害物検知の精度を向上させた研究。
著者: Matthias Brucker, Andrei Cramariuc, Cornelius von Einem, Roland Siegwart, Cesar Cadena
分類: cs.CV, cs.RO
原文アブストラクト
Reliable obstacle detection on railways could help prevent collisions that result in injuries and potentially damage or derail the train. Unfortunately, generic object detectors do not have enough classes to account for all possible scenarios, and datasets featuring objects on railways are challenging to obtain. We propose utilizing a shallow network to learn railway segmentation from normal railway images. The limited receptive field of the network prevents overconfident predictions and allows the network to focus on the locally very distinct and repetitive patterns of the railway environment. Additionally, we explore the controlled inclusion of global information by learning to hallucinate obstacle-free images. We evaluate our method on a custom dataset featuring railway images with artificially augmented obstacles. Our proposed method outperforms other learning-based baseline methods.
