Automated Detection of Manhole Covers in MLS Point Clouds Using a Deep Learning Approach
Why this work is in the frame
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Bibliographic record
Abstract
Road manhole cover works as an important part of road construction. Timely detection can make a great progress in the development of road management. This paper proposes a rapid road manhole detection method using mobile LiDAR with state-of-the-art computer vision and deep learning techniques. Firstly, the road surface data is extracted from mobile laser scanning system(MLS). Then, the 2D geographic reference feature(GRF) images are formed from 3D point cloud. Finally, the object detector using deep learning technology was applied to locate and annotate the road manholes. Also, we adjusted the training model to present the better result with high confidence over 0.90. Compared with the previous method, the proposed method can correctly detect the manhole cover with higher rate of precision and FI-feature at 0.952 and 0.975 respectively, especially in the complex road situation.
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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 it