Self reconfigurable facade cleaning robot equipped with deep learning based crack detection based on convolutional neural networks
Abstract 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 facade 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.