An Innovative Path for UAV Tilt Photography Image Processing Based on K-means Algorithm in Civil Engineering Disaster Management
Bibliographic record
Abstract
Civil engineering disasters mostly occur in mountainous areas, and it is difficult to comprehensively monitor them using traditional technology, while this drawback can be avoided by utilizing UAV inclined photogrammetry technology.In this paper, with the support of the relevant experimental equipment, we obtain the images of civil engineering disasters with the help of this technology, and in order to avoid the influence of the interference factors in the images on the research results, we propose to use the K-means algorithm to pre-process the images.After completing the image processing, the improved YOLOV4 target detection algorithm is used to complete the design of the intelligent detection model of civil engineering disasters, and the processed images are input into the model for iterative training, so as to realize the intelligent management and early warning of civil engineering disasters.A region in Yunnan Province is taken as an example to explore and analyze the example.As of 2022, it is found that 180 landslides actually appeared in the region, while the model detected 172 landslides, resulting in the model's civil engineering disaster detection accuracy of 95.56%, which is within the permissible range, proving that the model has a good application efficiency, and can provide certain help and innovative guidance for the relevant units of civil engineering disaster management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".