Recognition and Classification of Concrete Cracks under Strong Interference Based on Convolutional Neural Network
Bibliographic record
Abstract
This paper proposes the UmNet model based on convolutional neutral network (CNN), aiming to improve the ability to recognize and classify concrete cracks in a background complicated by construction seams and seepage traces. The model was derived from the famous CNN AlexNet. Without changing the receptive field, large convolutional kernels were replaced with small ones to reduce the parameters, deepen the network, and increase nonlinear transforms. Next, convolutional block attention module (CBAM) was introduced to highlight the key information in images and focus on high-weight channels. Finally, Bayesian network (BN) layer and L2 regularization were added, and the number of nodes in fully connected layer were reduced. A series of comparative experiments were carried out on three datasets D, P, and W. The results show that the proposed UmNet surpassed AlexNet in the recognition accuracy on D, P, and W by 3.74%, 3.17%, and 5.74%, respectively, and reduced the number of parameters by 75.04%. Therefore, our model is an effective means to recognize and classify of concrete cracks under strong interference.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".