CrackU‐net: A novel deep convolutional neural network for pixelwise pavement crack detection
Bibliographic record
Abstract
Periodic road crack monitoring is an essential procedure for effective pavement management. Highly efficient and accurate crack measurements are key research topics in both academia and industry. Automatic methods gradually replaced traditional manual surveys for more reliable evaluation outputs and better efficiency, whereas the devices are not available to all functional classes of pavements and different departments considering the high cost versus the limited budget. Recently, the widespread use of smartphones and digital cameras made it possible to collect pavement surface crack images at an affordable price in easier ways. However, the qualities of these crack images are diversely influenced by the noises from pavement background, roadways, and so forth. Thus, traditional methods usually fail to extract accurate crack information from pavement images. Therefore, this research proposes a state-of-the-art pixelwise crack detection architecture called CrackU-net, which is featured by its utilization of advanced deep convolutional neural network technology. CrackU-net achieved pixelwise crack detection through convolution, pooling, transpose convolution, and concatenation operations, forming the “U”-shaped model architecture. The model is trained and validated by 3,000 pavement crack images, in which 2,400 for training and 600 for validating, using the Adam algorithm. CrackU-net has the performance of loss = 0.025, accuracy = 0.9901, precision = 0.9856, recall = 0.9798, and F-measure = 0.9842 with learning rate of 10−2. Meanwhile, the false-positive crack detection problem is avoided in CrackU-net. Therefore, CrackU-net outperforms both traditional approaches and fully convolutional network (FCN) and U-net for pixelwise crack 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".