Drone Clean-Zilla
Bibliographic record
Abstract
The main reason for waste production is littering which also results in pollution. Knowingly or unknowingly, littering affects the climate and environment. And, it additionally costs the municipalities a huge amount of money every year to clean up the cities. Normally, garbage searching, collection and waste management is in the hands of the executives and authorities, for example, the municipality trucks work for urban communities, janitors for industries, etc. But, none of these systems are perfect. Because of the weaknesses of existing waste management system and less transparency in the garbage disposal system by the authorities, this paper aims to propose a solution for garbage checking in a wide manner. This paper presents an efficient solution for solid waste detection which will be made possible with the use of UAV i.e. Unmanned Aerial Vehicle (for example, drone) which will assist the society by distinguishing places polluted with waste materials and will send the area details as a warning in the “Drone Cleanzilla” website.This system will utilize the ideas of image stitching, deep learning, and convolutional neural networks to detect garbage from the pictures captured by the drone. These images will be sent to Firebase using Raspberry Pi which is going to be installed in the drone. From Firebase cloud, “Drone Cleanzilla” website will fetch the images with time and location. This project will bring more transparency to the wastage cleaning system as all the details of waste along with pictures and location will be uploaded in our website which will be accessible to everyone with internet. This will alert the municipality and officials and they will have to responsibly take actions to clean the garbage. In future, we will also examine the future uses of this project which will handle both trash identification and collection by the government authorities.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".