Integration of the Faster R-CNN Algorithm for Waste Detection in an Android Application
Bibliographic record
Abstract
The surge in global population and economic activity has precipitated a significant escalation in waste generation, with projections indicating potential daily global waste levels of 11 million tons by the century's conclusion. This intensification presents a formidable challenge requiring innovative solutions, one of which is the utilization of machine learning algorithms for automated waste categorization. This study explores the integration of the Faster R-CNN algorithm within an Android application, aimed at streamlining waste management through the identification and categorization of recyclables. The proliferation of smartphone usage-specifically Android applicationsprovides an accessible platform for mass education on waste management, thereby contributing to potential waste reduction. This study deployed a visual testing approach to evaluate the application's performance across diverse waste categories, including cardboard, glass, plastic, and paper. During each testing session, images of each waste type were captured and the objects were methodically rotated by approximately 20 degrees, enhancing the robustness of the machine learning model. The implementation of the Faster R-CNN algorithm within an Android environment, as exemplified in this study, has demonstrated noteworthy potential to revolutionize waste management. An impressive accuracy rate of 98.106% was achieved in the detection and classification of various waste types. Such a technological innovation can augment the efficiency of waste categorization and recycling efforts. Thus, the study contributes to the development of a more sustainable and environmentally responsible waste management system.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".