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首页> 外文期刊>Automation in construction >Self-reconfigurable facade-cleaning robot equipped with deep-learning-based crack detection based on convolutional neural networks
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Self-reconfigurable facade-cleaning robot equipped with deep-learning-based crack detection based on convolutional neural networks

机译：基于卷积神经网络的基于深度学习的裂缝检测的可重构门面清洁机器人

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摘要

Despite advanced construction technologies that are unceasingly filling the city-skylines with glassy high-rise structures, maintenance of these shining tall monsters has remained a high-risk labor-intensive process. Thus, nowadays, utilizing fa ade-cleaning robots seems inevitable. However, in case of navigating on cracked glass, these robots may cause hazardous situations. Accordingly, it seems necessary to equip them with crack-detection system to eventually avoid cracked area. In this study, benefitting from convolutional neural networks developed in TensorFlow (TM), a deep-learning-based crack detection approach is introduced for a novel modular facade-cleaning robot. For experimental purposes, the robot is equipped with an on-board camera and the live video is loaded using OpenCV. The vision-based training process is fulfilled by applying two different optimizers utilizing a sufficiently generalized data-set. Data augmentation techniques and also image pre-processing also apply as a part of process. Simulation and experimental results show that the system can hit the milestone on crack-detection with an accuracy around 90%. This is satisfying enough to replace human-conducted on-site inspections. In addition, a thorough comparison between the performance of optimizers is put forward: Adam optimizer shows higher precision, while Adagrad serves more satisfying recall factor, however, Adam optimizer with the lowest false negative rate and highest accuracy has a better performance. Furthermore, proposed CNN's performance is compared to traditional NN and the results provide a remarkable difference in success level, proving the strength of CNN.
机译：尽管先进的建筑技术不断用玻璃状的高层建筑填充城市天际线，但维护这些光亮的高大怪物仍然是高风险的劳动密集型过程。因此，如今，使用褪色清洁机器人似乎是不可避免的。但是，如果在破裂的玻璃上导航，这些机器人可能会导致危险情况。因此，似乎有必要为其配备裂纹检测系统以最终避免裂纹区域。在这项研究中，得益于TensorFlow（TM）开发的卷积神经网络，针对新型模块化立面清洁机器人引入了一种基于深度学习的裂缝检测方法。为了进行实验，该机器人配备了车载摄像头，并使用OpenCV加载了实时视频。基于视觉的训练过程是通过使用两个充分利用通用数据集的不同优化器来实现的。数据增强技术以及图像预处理也适用于过程。仿真和实验结果表明，该系统可以达到裂纹检测的里程碑，其准确性约为90％。这足以代替人工进行的现场检查。此外，还对优化器的性能进行了彻底的比较：Adam优化器显示出更高的精度，而Adagrad提供了更令人满意的召回因子，但是，假阴性率最低，准确性最高的Adam优化器具有更好的性能。此外，将拟议的CNN的性能与传统的NN进行了比较，结果在成功水平上提供了显着的差异，证明了CNN的实力。

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