Mechanical Structure Defect Detection after High Temperature Based on Image Processing
Bibliographic record
Abstract
Mechanical structures may exhibit defects during long-time high-temperature operation.Infrared image technology quickly and automatically detects mechanical structure defects, improves detection speed and accuracy, and reduces the workload of manual detection.Although high-temperature infrared image processing method has made significant progress in detecting the defects, it still has some shortcomings.Defect features in high-temperature infrared images may not be obvious and are mixed with background information, thus making it difficult to accurately identify and extract them.Therefore, this research studied the detection method of mechanical structure defects after high temperature based on image processing.Transform domain denoising method was used to decompose the transform domains of images, which distinguished signals and noises in the images.Adaptive Contrast Enhancement (ACE) algorithm was used to enhance the images.A feature fusion imaging detection framework for infrared and optical imaging of high-temperature mechanical structures was constructed, which improved the accuracy and reliability of defect detection.Deep neural network was combined with the heuristic fusion section, which further explored deep features in the images and improved the fusion effects.The proposed fusion features were processed using binary tree classification and hierarchical classifier, which accurately identified the abnormal defect regions in the infrared images of high-temperature mechanical structures.The experimental results verified that the proposed method was effective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".