Balanced X-ray Security Dataset and Enhanced YOLO for Contraband Detection
Bibliographic record
Abstract
To address critical challenges in X-ray contraband detection-including severe class imbalance in existing datasets, scarcity of high-quality annotated data, and poor model adaptability to complex scenarios-this study first constructs a balanced X-ray contraband detection dataset. Derived from the SIXray and PIDray datasets, the balanced dataset comprises 13,728 images covering 12 different contraband categories. To resolve class imbalance, a Class-Specific Augmentation Framework (CSAF) with four physical transformations and random undersampling are adopted, ensuring approximately 1,500 samples per category for uniform class distribution. Two improved models (ASEA-Net and CSEC-Net) based on YOLOv11s are proposed for lightweight and high-precision contraband detection tasks. Experiments on the balanced dataset show that ASEA-Net achieves 95.78% accuracy and 93.55% mAP@50, outperforming YOLOv11s by 1.46% and 1.37% respectively with 13.37% fewer parameters; CSEC-Net reduces parameters by 39.91% and FLOPs by 40.38% compared to YOLOv11s, enabling deployment on resource-constrained edge devices. Both models exhibit strong performance in complex scenarios, validating the value of the balanced dataset and the effectiveness of the proposed models for X-ray contraband detection.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".