Abstract
Using deep learning to assist people in recognizing prohibited items in X-Ray images is crucial to improve the quality of security inspections. However, these methods require lots of data and the data collection usually takes much time and efforts. In this paper, we propose a method to synthesize X-ray image to support the training of prohibited items detectors. The proposed framework is built on the Generative Adversarial Networks (GAN) with multiple discriminators, trying to synthesize realistic X-Ray prohibited items and learn the background context simultaneously. In the other hand, a guided filter is introduced for detail preserving. The experimental results show that our model can smoothly synthesize prohibited items on background images. To quantitatively evaluate our approach, we add the generated samples into training data of the Single Shot MultiBox Detector (SSD) and show the synthetic images are able to improve the detectors’ performance.
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Zhao, T., Zhang, H., Zhang, Y., Yang, J. (2019). X-Ray Image with Prohibited Items Synthesis Based on Generative Adversarial Network. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_42
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