運用人工智慧偵測方法增強機場安全：結合不同X光能量之先導研究

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运用人工智慧侦测方法增强机场安全：结合不同X光能量之先导研究

Enhancing Airport Security through AI-based Detection：A Pilot Study Combining Different X-ray Energy

刘建利 , 硕士　　导师：黄咏晖　　

繁体中文

人工智慧；深度学习；多能量X光图像；航空安全； Artificial intelligence ； deep learning ； multi-energy x-ray images ； aviation security

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摘要 │ 参考文献 (13) │ 国际计量

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Purpose： Baggage inspection is critical for ensuring aviation safety. Traditional inspection methods require human inspectors to screen a large volume of X-ray images, leading to low accuracy and efficiency. This study aims to address these challenges by developing an automated, high-accuracy recognition system for aviation baggage inspection. This system integrates multi-energy X-ray scanning with artificial intelligence (AI) techniques, enhancing both passenger safety and anti-smuggling efforts.
Materials and Methods： In this pilot study, we acquired twenty-six X-ray images of items commonly smuggled, such as garlic, mushrooms, pork products, tobacco, and alcohol. The images were captured using X-ray parameters of 60, 80, 120 kV, a tube current of 10 mA, and exposure times of 100 and 600 msec. We synthesized three different energy images into a single X-ray image, which was colorized using the RGB color model; these images were labeled as the multi-energy image group (JPGH). Another set of images, directly pseudo-colored, formed the pseudo-color image group (JPGC). To train the YOLOv4 model, these images were augmented through horizontal and vertical flipping and intensity modulation, resulting in 1170 augmented images. The model was then trained to automatically recognize organic (O) and non-organic (NO) goods in both the JPGH and JPGC groups. Metrics such as hit rate and intersection over union (IoU) were employed for validation.
Results： The hit and Hit Rate for organic and non-organic goods in the JPGH group were 2.038 and 1.200 times better, respectively, compared to the JPGC group. Additionally, the IoU for organic and non-organic goods in the JPGH group was 1.090 and 1.046 times better, respectively, than that in the JPGC group. Hence, multi-energy X-ray images showed superior recognition performance and accuracy when processed through Fully Convolutional Network (FCN) AI models.
Conclusion： The integration of AI techniques with multi-energy X-ray images offers a more efficient and accurate approach to aviation security inspection. Well-trained deep learning algorithms can accurately identify dangerous and prohibited items, thereby reducing the rates of false positives and negatives. This pioneering research serves as a foundation for future applications of AI in enhancing aviation security.

参考文献 ( 13 ) 〈TOP〉

1. Vukadinovic, D.a.A., X-ray Baggage Screening and Artificial Intelligence (AI) . Publications Office of the European Union, 2022.
2. Mery, D., D. Saavedra, and M. Prasad, X-Ray Baggage Inspection With Computer Vision：A Survey. IEEE Access, 2020. 8：p.145620-145633.
3. Michel, S., et al. Computer-Based Training Increases Efficiency in X-Ray Image Interpretation by Aviation Security Screeners. in 2007 41st Annual IEEE International Carnahan Conference on Security Technology. 2007.
4. Zhiyu, C., et al. A Combinational Approach to the Fusion, De-noising and Enhancement of Dual-Energy X-Ray Luggage Images.in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops. 2005.
5. Saavedra, D., S. Banerjee, and D. Mery, Detection of threat objects in baggage inspection with X-ray images using deep learning. Neural Computing and Applications, 2021. 33 (13)：p. 7803-7819.

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使用DOI作为永久连结

引用含有DOI的文献

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。 如想对DOI的使用与概念有进一步了解，请参考 （ ） 。

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。如想对DOI的使用与概念有进一步了解，请参考（）。