Detection of Rail-track and Floodwater in UAV Imaging sensors Using Deep Learning
Bibliographic record
Abstract
The task of mapping and monitoring water bodies near railway tracks is crucial for railway safety. Accumulation of water near rail-tracks may lead to problems such as washout which may in turn cause accidents and damage to life or goods. Recently, unmanned aerial vehicle (UAV) have gained popularity for monitoring of such water-related hazards around rail-tracks. This research work investigates the effectiveness of a fully convolutional encoder-decoder type network based on U-Net for automated segmentation of rail-track and water regions from UAV-based imaging sensor. Through experimental evaluations using real-world datasets, the performance of the U-Net in segmenting rail-track and water regions is performed. On the Water & Rail-Track (WRT) dataset, the best performance of 0.545 and 0.673 mIoU is achieved for rail-tracks and water classes respectively. The best performance on the challenging Augmented-VOC Dataset is around mIoU of 0.9820 and 0.5283 for the rail-track and water classes respectively
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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".