Semantic Segmentation of Defects in Infrared Thermographic Images of Highly Damaged Concrete Structures
Bibliographic record
Abstract
There is a global research trend to enhance condition assessment of the concrete infrastructure by the development of advanced nondestructive testing (NDT) methods. Computer vision–based systems have been developed to detect different types of defects in both regular and thermographic images because these systems could offer a timely and cost-effective solution and are able to tackle the inconsistency issues of manual assessment. This paper investigates the performance of different deep neural network models to detect main concrete anomalies, including delamination, cracks, spalling, and patches in thermographic and regular images captured from a variety of distances and viewpoints. These models were trained and tested using images taken from a century-old buttress dam and validated in images captured from the decks of two concrete bridges. The results showed that the MobileNetV2 had promising performance in the identification of multiclass damages in the thermal images, identifying 79.7% of the total delamination, cracks, spalling, and patches on the test images of highly damaged concrete areas. The VGG 16 model showed better precision by reducing the number of false detections.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".