Vision Based Crack Detection in Concrete Structures Using Cutting-Edge Deep Learning Techniques
Bibliographic record
Abstract
Concrete crack detection is the process of inspecting the concrete structures. If the defects present in any structures could not be detected in time, it may have a severe impact. The cracks can be detected using destructive as well as non-destructive testing (NDT) techniques. This article presents image based NDT techniques for detecting the concrete cracks using the cutting edge deep learning techniques. NDT is the process of analysing the materials, components, structures etc. without causing any damage to it. In this paper, a transfer learning technique is proposed for detecting the cracks in the concrete structures. A dataset of 40000 images of concrete which is collected from METU Campus is analysed in NVIDIA Tesla V100 12 GB GPU servers using various recent deep learning techniques and the results are tabulated. Performance of four pre trained network architectures such as Alexnet, VGG16, VGG19 and ResNet-50 is assessed for categorizing the images. From the results, it is revealed that the residual neural network technique is successful in detecting the cracks with high accuracy and less complexity.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".