EGY_PDD: a comprehensive multi-sensor benchmark dataset for accurate pavement distress detection and classification
Bibliographic record
Abstract
Abstract Automated detection of pavement distresses using road images remains a research hotspot within the computer vision community. The advent of deep learning has sparked significant interest in enhancing the effectiveness of automated identification and assessment of pavement distresses. Yet, the limited availability of comprehensive ground truth datasets for pavement distresses poses a prominent challenge for training deep learning models. To address this issue, this study introduces the Egyptian Pavement Distress Dataset (EGY_PDD), a publicly available dataset that comprises images of various types of pavement distress, such as cracks, potholes, and rutting, collected from different regions in Egypt. The dataset is annotated with labels that indicate the type of the pavement distress in each image, making it suitable for training and evaluating machine learning models designated for automatic pavement distress detection and classification. The EGY_PDD dataset has some unique features, such as its focus on pavement distress problems commonly found in Egypt and the MENA (Middle East and North Africa) region, which experiences distinct pavement challenges due to specific geographical, climatic, and socioeconomic factors. EGY_PDD aims to create a comprehensive dataset that enables the development of more robust and easily deployable pavement condition assessment systems. The dataset includes annotated 2D images and 3D road scenes captured for the same pavement segments. Both 2D and 3D images are employed for distress detection and classification using deep learning frameworks. While 2D images contribute to these tasks, 3D images provide more precise classification of distress severity and more accurate calculations of density. These enhanced measurements from 3D images are crucial for the automated computation of pavement ratings or the Pavement Condition Index (PCI). The dataset, consisting of 14,612 meticulously annotated 2D images categorized into eleven distinct types of distresses, was evaluated using two iterations of the widely adopted deep learning framework, You Only Look Once (YOLO). The models, trained for no more than 300 epochs, achieved mAP50 and mAP50-95 scores of 0.617 and 0.293, respectively, demonstrating their adequate performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".