Equalized shape feature enhancement method for multiple ferromagnetic objects

When magnetic measuring instruments are used to measure the shape of ferromagnetic objects, the objects far away from the observation plane are likely to have blurred shape features because the magnetic field decays rapidly with distance. A bigger challenge is to measure multiple objects at the same time. When the relative positions of multiple measured objects and the observation plane are inappropriate, it is easy to have problems that the shape features of the deeper measured objects are not obvious and the magnetic signals of multiple measured objects are aliased, which usually leads to shape feature measurement failed. To address this issue, we propose an equalized shape feature enhancement method for multiple ferromagnetic objects. The method enhances shape features by evaluating the trends of the total horizontal derivative and vertical derivative of the magnetic field within the measurement area using the standard deviation. Meanwhile, the method combines the theory of ratio equalization and normalization to improve the shape features convergence of deeper objects and balance the signal aliasing interference between objects of different depths. Model simulation and experimental results show that the shape feature measurement results of the proposed method are clear and in good agreement with the ideal model. The method can effectively balance the magnetic anomaly amplitudes of the measured objects with different depths, and improve the accuracy and stability of shape feature measurement. We compare and analyze the processing effects of the proposed method and the traditional normalized standard deviation method (NSTD). It is calculated that the standard deviations of the results obtained by the proposed method and the NSTD method are 0.146 and 0.136, and the average peak-to-trough differences are 0.368 and 0.352, respectively. Therefore, the proposed method can better enhance the shape characteristics of ferromagnetic objects and has more practical application value.