Self-reconfigurable façade-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 façade-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™, a deep-learning-based crack detection approach is introduced for a novel modular façade-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.