Computer-Vision-Based Statue Detection with Gaussian Smoothing Filter and EfficientDet
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smart tourism is a developing industry, and numerous nations are planning to establish smart cities in which technology is employed to make life easier and link nearly everything. Many researchers have created object detectors; however, there is a demand for lightweight versions that can fit into smartphones and other edge devices. The goal of this research is to demonstrate the notion of employing a mobile application that can detect statues efficiently on mobile applications, and also improve the performance of the models by employing the Gaussian Smoothing Filter (GSF). In this study, three object detection models, EfficientDet—D0, EfficientDet—D2 and EfficientDet—D4, were trained on original and smoothened images; moreover, their performance was compared to find a model efficient detection score that is easy to run on a mobile phone. EfficientDet—D4, trained on smoothened images, achieves a Mean Average Precision (mAP) of 0.811, an mAP-50 of 1 and an mAP-75 of 0.90.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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