An Efficient Approach to Human Security Screening Image Recognition Through a Lightweight CNN Utilizing Yolov5s and GhostNet
Bibliographic record
Abstract
Human security screening constitutes a vital component in public safety assurance across varied environments like airports, governmental edifices, and additional public spaces.Among the paramount challenges inherent in human security screening lies the immediate and precise discernment of prospective threats within X-ray images.Despite the potential exhibited by convolutional neural networks (CNNs) in image recognition tasks, including the detection of targets in X-ray imagery, the substantial computational burden and memory prerequisites often render real-time deployment impracticable on devices with limited resources.In the present study, a novel lightweight CNN approach, melding Yolov5s and GhostNet models with the coordinate attention mechanism, is introduced to alleviate the constraints found in existing techniques.By employing this combination, efficiency in computation and model accuracy has been augmented, thereby addressing the challenges of swift and accurate threat identification.Performance evaluation, conducted on a publicly accessible dataset comprising X-ray images pertinent to human security screening, demonstrated the superior detection accuracy and reduced storage footprint of the proposed model in comparison to prevailing alternatives.Overall, the approach delineated herein presents an efficacious and streamlined solution for real-time human security screening image recognition on resource-constrained devices, contributing a promising advancement in the field.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".