Improved YOLOv5-based multi-crack detection in concrete wall surfaces
Bibliographic record
Abstract
In engineering and infrastructure management, it is essential to ensure the safety and longevity of structures. This can be carried out by properly detecting and monitoring surface cracks. From this lens, this paper presents a novel method for identifying surface cracks using the YOLOv5 (You Only Look Once) deep learning architecture to provide real-time object detection capabilities. First, the YOLOv5 was used with predefined hyperparameters to evaluate its pre-trained knowledge in concrete cracking. Then, the YOLOv5 architecture was fine-tuned to accommodate the specific characteristics of surface cracks within concrete structural components. Finally, the model's backbone was replaced with ResNet-50, and its performance was examined. The experiments in this study involved a diverse dataset of surface crack images and aimed to compare the performance of the three approaches in terms of Precision, Recall, and mean Average Precision metrics. Our findings indicate that YOLOv5-based approaches possess good surface crack identification, with the backbone replacement approach demonstrating the potential for improved adaptability to various structural environments. By combining the capabilities of YOLOv5 and the training strategies, the approach enhances the accuracy and reliability of the surface crack detection systems, resulting in the overall safety and durability of critical infrastructure.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".