Novel Smart Waste Sorting System based on Image Processing Algorithms: SURF-BoW and Multi-class SVM
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Bibliographic record
Abstract
Aiming at solving the waste sorting problems of smart environmental sanitation, this paper proposes a novel smart waste sorting system, which consists of two sub-systems including a hardware system and a software system. The hardware system is of a trash bin framework based on the core module Raspberry Pi and the software one is of an image classification algorithm platform based on SURF-BoW algorithm and multi-class SVM classifier. In our experiment, the images produced during training and testing are both obtained from webcam in our system and extra processing with affine transformation and noise-adding operation. The experimental results show that among the five categories of waste, the battery waste performs best with 100% classification accuracy. Besides, the average classification accuracy is up to 83.38%. Therefore, our system has reliable practicability and robustness, which is expected to be applied to deal with the waste sorting problems in our daily life.
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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 | 0.001 | 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 | 0.001 | 0.012 |
| Open science | 0.001 | 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