Research on Dynamic Target Detection and Adaptive High-Rate Compression Technology for Power Smart Site Monitoring Video Based on Artificial Intelligence Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
With the deepening of the exploration of informationization in the construction industry, the smart construction site comes into being under the support of technological development and policy. The article combines artificial intelligence technology with electric power smart site, and deeply researches the application of artificial intelligence technology in electric power smart site. For the security monitoring in the smart construction site, a SSD7-FFAM lightweight target detection method is proposed based on the SSD7 algorithm, using feature fusion and attention mechanism methods. Then, based on the fast acquisition of temporal information of surveillance video scenes, an adaptive compression technique with wavelet sparse measurement is designed. Through the model comparison analysis, the SSD7-FFAM algorithm achieves better detection effect and detection speed of 84.97% and 83.45FPS in real application scenarios, and has a smaller number of parameters and computation.The AVCS method can be effectively adaptively adopted, and most of the reconstructed image PSNR values of this method are greater than 40dB under different sampling rates, and the quality of the reconstructed image is better than the Contrast compression technique, which can be used for the high rate compression of intelligent construction site monitoring video. The research results will provide informative ideas for construction companies to introduce AI technology in power smart construction sites.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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