Drug Recognition Detection Based on Deep Learning and Improved YOLOv8
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Identifying drugs from surveillance or other videos presents challenges such as small target sizes, class imbalance, and similarities to other objects. Additionally, the hardware used to capture videos and the video resolution and clarity limit model scalability, leading to poor detection accuracy in traditional models. To address this issue, we propose an improved YOLOv8s-based model. The experimental outcomes reveal that the improved YOLOv8s model attains a precision of 95.1% and a mAP@50 of 87.4% in drug detection and identification, representing improvements of 3.0% and 2.2% over the original YOLOv8s model. The proposed improvements to YOLOv8s effectively boost detection accuracy and recognition rates while preserving high efficiency. This model demonstrates superior overall detection performance compared to other algorithms, providing fresh perspectives and methods for advancing research and applications in drug detection and recognition.
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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 | 0.000 | 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) | 0.000 | 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