Abstract Enclosed spaces are common in built structures but pose a challenge to many forms of manual or robotic surveying and maintenance tasks. Part of this challenge is to train robot systems to understand their environment without human intervention. This paper presents a method to automatically classify features within a closed void using deep learning. Specifically, the paper considers a robot placed under floorboards for the purpose of autonomously surveying the underfloor void. The robot uses images captured using an RGB camera to identify regions such as floorboards, joists, air vents and pipework. The paper first presents a standard mask regions convolutional neural network approach, which gives modest performance. The method is then enhanced using a two-stage transfer learning approach with an existing dataset for interior scenes. The conclusion from this work is that, even with limited training data, it is possible to automatically detect many common features of such areas.

中文翻译：

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使用带有两阶段转移学习的掩模区域卷积神经网络对地板下场景进行图像分割

摘要封闭空间在建筑结构中很常见，但对多种形式的手动或机器人测量和维护任务构成了挑战。这一挑战的一部分是训练机器人系统在没有人工干预的情况下了解其环境。本文提出了一种使用深度学习对封闭空间内的特征进行自动分类的方法。具体而言，该论文考虑了放置在地板下的机器人，其目的是自主测量地板下的空隙。该机器人使用 RGB 摄像头捕获的图像来识别地板、托梁、通风口和管道等区域。该论文首先提出了一种标准的掩码区域卷积神经网络方法，它提供了适度的性能。然后使用两阶段转移学习方法和现有的室内场景数据集来增强该方法。