Enhanced Cigarette Pack Counting via Image Enhancement Techniques and Advanced SAFECount Methodology
Bibliographic record
Abstract
In the realm of cigarette pack counting systems, prevalent challenges persist, notably the low accuracy in count, limited adaptability to intricate scenes and varying environments, and a lack of responsiveness to diverse pack types and shapes.This study introduces an advanced method for cigarette pack counting, leveraging a combination of various image enhancement techniques and an improved Similarity-Aware Feature Enhancement block for object Counting (SAFECount) approach.The methodology comprises three integral modules: an image enhancement module, a feature extraction module, and a counting module.The image enhancement module, tasked with noise reduction and deblurring, ensures targeted enhancement effects on cigarette box images.To contend with the rapid shifts in cigarette pack appearances, this research integrates specialized color and boundary feature extraction networks with the SAFECount method.This integration facilitates the fusion of multi-scale, key semantic information, thus amplifying the model's detection efficacy.Addressing the scalability limitations prevalent in general models, the study employs a few-shot counting (FSC) approach, which endows the model with essential generalization and flexibility, requisite for practical applications, even with a minimal training dataset.Empirical analyses, conducted using actual data from the Zhongyan Corporation's cigarette pack dataset, substantiate the superiority of the proposed method in real-world warehouse environments.The method demonstrates a marked improvement in counting performance, evidenced by a Mean Absolute Error (MAE) of 1.71 and a Root Mean Square Error (RMSE) of 1.95.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".