Enhancing Accuracy for Metal Target Detection Using CNN-GP Algorithm

Inversion based on electromagnetic induction EMI is an important method for detection of underground metal targets in fields such as archaeology and geological exploration. However, traditional inversion algorithms grounded in the framework of least squares suffer from long iteration times, susceptibility to local optima, and dependence on initial values. To address these challenges and enhance the detection of underground metal target detection, this paper proposes an innovative CNN-GP algorithm based on Convolutional Neural Network CNN and Gaussian Process GP . Our proposed algorithm initiates by extracting discriminative features based on CNN, followed by dimensionality reduction through a multilayer perceptron MLP to map the extracted features into low-dimensional vectors, and estimating the position of metal targets through GP algorithm. To refine the accuracy of the CNN-GP algorithm, this paper uses grid and Bayesian search algorithms for network optimization. Results demonstrate that the Bayesian search algorithm expeditiously identifies an optimal set of hyperparameters, yielding inversion performance compared with grid search algorithm. Comparative analyses of inversion efficacy between CNN, GP, MLP, and CNN-GP algorithms pre- and post-optimization reveal CNN-GP as the optimal performer, with inversion errors of 0.5cm, 0.5cm, and 2.4cm along the x , y , and z direction, respectively.