ロボティクス・メカトロニクス講演会講演概要集
Online ISSN : 2424-3124
2016
セッションID: 1A2-09a7
会議情報

Scene-parsing for disaster environments by means of a convolutional neural network
Solvi ArnoldKimitoshi Yamazaki
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会議録・要旨集 フリー

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Disaster robotics poses particular challenges for computer vision, both in terms of image material characteristics (due to motion blur, difficult light conditions, lack of up/down orientation, etc.), and in terms of learning data (limited availability, difficulty of annotation due to image quality, etc.). We developed a system for real-time scene-parsing, intended for use in a support system for operators of remote-controlled mobile robots employed in disaster areas. Our testbed is video footage gathered by a snake-like mobile robot exploring an (artificial) collapsed building environment. The core of the system is a relatively small-scale convolutional neural network. Our approach combines pixel-level learning with superpixel-level classification, in an effort to learn efficiently from a relatively small number of partially annotated frames. Our classification system is capable of real-time operation, and demonstrates that convolutional neural networks can be employed effectively even under the harsh conditions imposed by disaster robotics.

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© 2016 The Japan Society of Mechanical Engineers
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