UnderstAnding Bag of Tricks of Deep Learning-Based Semantic Segmentation in Pavement Crack Detection
Bibliographic record
Abstract
The rapid development of deep learning has significantly enhanced the performance of models in the detection of pavement cracks, thereby facilitating the deployment of deep learning-based approaches into real-world applications. Nevertheless, it is worth noting that deep learning-based crack detection models represent complex amalgamations of deep learning networks and model training strategies, with the latter frequently being overlooked. Therefore, in this paper, we focus on various techniques in data augmentation, and model deployment stages that are commonly employed in deep learning-based semantic segmentation models. Through extensive experiments, the effectiveness of these techniques in crack detection is evaluated, aiming to provide guidance for subsequent crack detection experiments and project implementations. Consequently, the experiments demonstrate data augmentation methods such as color jittering and CutMix can effectively improve model performance by altering the distribution of the training dataset. Additionally, in case of crack datasets with limited samples and severe class imbalance, loss function selection and pre-training weights can be crucial in model deployment.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".