Self-reconfigurable façade-cleaning robot equipped with deep-learning-based crack detection based on convolutional neural networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it