自律移動ロボット向け高速画像異常軽減
Fast Image-Anomaly Mitigation for Autonomous Mobile Robots
雨や埃によるカメラ画像の劣化をリアルタイムで軽減する前処理手法を提案し、既存手法より最大40倍高速で高精度を実現した。
著者: Gianmario Fumagalli, Yannick Huber, Marcin Dymczyk, Roland Siegwart, Renaud Dubé
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
Camera anomalies like rain or dust can severelydegrade image quality and its related tasks, such as localizationand segmentation. In this work we address this importantissue by implementing a pre-processing step that can effectivelymitigate such artifacts in a real-time fashion, thus supportingthe deployment of autonomous systems with limited computecapabilities. We propose a shallow generator with aggregation,trained in an adversarial setting to solve the ill-posed problemof reconstructing the occluded regions. We add an enhancer tofurther preserve high-frequency details and image colorization.We also produce one of the largest publicly available datasets1to train our architecture and use realistic synthetic raindrops toobtain an improved initialization of the model. We benchmarkour framework on existing datasets and on our own imagesobtaining state-of-the-art results while enabling real-time per-formance, with up to 40x faster inference time than existingapproaches.
