Deep Learning-Enabled Automatic Detection of Bridges for Promoting Transportation Surveillance under Different Imaging Conditions
Bibliographic record
Abstract
The reliable and accurate detection of bridges plays an important role in imaging-driven transportation surveillance. It is capable of timely providing the traffic information, leading to safer and more convenient transportation. However, the visual quality of observed images is often inevitably reduced owing to the adverse weather conditions, e.g., haze and low lightness. It is still difficult to adopt the existing powerful deep learning methods to reliably and accurately detect the bridges under different imaging conditions. To achieve satisfactory bridge detection results, we first propose to exploit the data augmentation strategy and physical imaging method to generate the natural-looking experimental dataset, which contains latent high-quality images and their hazy and low-light versions. We then investigate how to further promote the deep learning-based bridge detection methods through the manually generated dataset. It is obvious that the generalization abilities of these deep neural networks are significantly improved using this data augmentation strategy. In this work, we constructed an original dataset consisting of 3500 images of size 900×600, collected under normal imaging condition. Extensive detection experiments will be performed based on the augmented dataset. Experimental results have demonstrated that our automatic bridge detection framework could generate more reliable and accurate results compared with existing detection methods.
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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 | 0.000 |
| 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".