Figures (4)  Tables (5)

    • Figure 1. 

      Comparison of detection accuracy of different algorithms.

    • Figure 2. 

      Comparison of false alarm rate of different algorithms.

    • Figure 3. 

      Comparison of missed alarm rate of different algorithms.

    • Figure 4. 

      Comparison of detection performance between WGAN and GAN.

    • Metal foreign body type Material Shape Distance from transmitter (cm) Interference level
      Foreign body 1 Iron Round 5 High
      Foreign body 2 Aluminum Square 10 Medium
      Foreign body 3 Copper Irregular 15 Low

      Table 1. 

      Different types of metallic foreign bodies used in the experiment and their corresponding effects.

    • Algorithm Accuracy Recall rate F1 score
      GANs algorithm in this article 98.5% 96.8% 97.6%
      Electromagnetic detection algorithm 91.2% 88.5% 89.8%
      Image detection algorithm 87.6% 85.3% 86.4%

      Table 2. 

      The proposed algorithm with other traditional detection algorithms.

    • Algorithm False alarm rate Missing report rate
      GANs algorithm in this article 1.5% 2.3%
      Electromagnetic detection algorithm 5.8% 6.4%
      Image detection algorithm 7.3 8.1

      Table 3. 

      Comparison results of false and missed alarm rates of different algorithms in the same test scenario.

    • Generator architecture Accuracy Recall rate F1 score
      Original architecture 98.5% 96.8% 97.6%
      Increase the number of layers 98.9% 97.2% 98%
      Adjust the convolution kernel size 98.2% 96.5% 97.3%

      Table 4. 

      Performance of the generator under different configurations.

    • Discriminator depth Accuracy Recall rate F1 score
      Original depth 98.5% 96.8% 97.6%
      Increase depth 99% 97.5% 98.2%
      Decrease depth 97.8% 95.4% 96.6%

      Table 5. 

      Detection performance at different discriminator depths.