Research on X-ray security contraband identification technology based on lightweight YOLOv8
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View abstract on PubMed
Summary
This summary is machine-generated.A new lightweight algorithm, YOLO-CBF, enhances X-ray contraband detection by optimizing YOLOv8. This method improves accuracy and reduces computational costs for security screening.
Area Of Science
- Computer Vision
- Artificial Intelligence
- Security Technology
Background
- X-ray security screening faces challenges in contraband identification and localization.
- Upgrading security equipment is crucial during peak operational periods.
Purpose Of The Study
- To develop a lightweight X-ray security contraband detection algorithm optimized for YOLOv8.
- To improve the efficiency and accuracy of contraband detection in X-ray images.
Main Methods
- Reduced network channel count to decrease model parameters and computational complexity.
- Introduced a simplified Bi-directional Feature Pyramid Network (BiFPN) for multi-scale feature integration.
- Developed Focal-GIoU Loss to address sample imbalance and focus on difficult targets.
Main Results
- Achieved improved mean Average Precision (mAP) on PIDray datasets (1.5%, 0.9%, 0.5%).
- Reduced parameter count from 3.0M to 2.1M and computational cost from 8.1G to 6.3G.
- Demonstrated strong performance in comparative and cross-dataset generalization experiments.
Conclusions
- The proposed YOLO-CBF algorithm offers an effective and efficient solution for X-ray contraband detection.
- The optimizations enhance detection accuracy, particularly for challenging targets like small or occluded items.
- The lightweight design facilitates easier deployment and upgrading of security equipment.
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