Research on Insulator Defect Detection Model Based on Improved YOLOv8
Bibliographic record
Abstract
Electricity is the basis for everyday life, and defect detection of insulators of high-voltage transmission lines is the key to ensuring power transmission. In order to overcome the problems of low accuracy of traditional target detection algorithms for small targets, weak representation ability of feature maps and little key information extraction, an improved CBAM attention-based insulator defect detection method CBAM-YOLOv8 based on YOLOv8 was proposed. The core is to apply the combination of Channel Attention Module and Spatial Attention Module to process the channel attention and spatial attention modules respectively for the input feature layers. Experiments show that the AP in the CPLID dataset is improved by 4.7% and the FPS is reduced by 2.7% compared with the original YOLOv8, which proves that the proposed method can maintain a high detection speed while greatly improving the detection accuracy, providing a more effective and safer solution for the detection of high-voltage transmission lines, and greatly reducing the labor cost and operation risk.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".