Automatic Classification and Identification of Road Garbage Images and Evaluation of Environmental Health Based on UNet++
Bibliographic record
Abstract
As Covid-19 plagues the world, a clean environment helps to control the factors and risks that threaten health, and curb the spread of the epidemic. However, the quality evaluation of environmental health faces some problems and challenges in actual management and practice. Firstly, the classification, identification, and quantification of road garbage are mainly done manually, because of the diversity of road garbage, as well as their sharp differences in geometry, color, and texture. Secondly, it is labor-intensive to manually manage the large operation areas on the wide urban roads. Thirdly, the accuracy of statistical indices is affected by the time-varying road environment, making the quality evaluation of environmental health untimely and inaccurate. To solve these problems, this paper proposes an intelligent image classification and evaluation method for urban environmental health. Specifically, an environmental garbage recognition and semantic segmentation approach was designed based on UNet++, and combined with the vehicle-mounted machine vision system to automatically identify the typical targets among the road waste control indices. Next, an image attention quantitative evaluation method was developed based on the eye tracking analyzer, and the quantified attention was fused with the statistical features for road garbage classification, forming an attention-based evaluation method for environmental quality. The proposed approach supports the automatic recognition and semantic segmentation of the garbage on urban roads, and realizes the identification of complex targets in different scenes through transfer learning. In addition, the attention-based evaluation method for environmental quality provides environmental management departments with visual basis for quantitative decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".