Instance-Based Stacked Generalization for Transfer Learning

Baghoussi, Yassine; Mendes-Moreira, João

doi:10.1007/978-3-030-03493-1_78

Cited by 1 publication

(1 citation statement)

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“…In this module, the training of the cavity identification model has been completed by model-based transfer learning. Modelbased transfer learning methods use the knowledge learned by the model in the source domain as the support for the identification of the model in the target domain [41]. With similar local and overall characteristics in the source and target domains, the network structure and parameters are shared at the model and parameter levels [42] by pre-training and fine-tuning [43].…”

Section: Research On Algorithmmentioning

confidence: 99%

3D ground penetrating radar cavity identification algorithm for urban roads using transfer learning

Li¹,

Yang²,

Qiao³

et al. 2023

Meas. Sci. Technol.

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3D ground penetrating radar (GPR) is the main method for the detection of underground cavities in urban roads. The number of road cavity samples detected by 3D radar is small, whereas the intelligent identification model require a large number of learning samples for model training, resulting in inadequate model training. This causes the model to be less accurate in identifying cavities, resulting in many misses and misjudgments. Given the above problems, combined with the detection characteristics of the vertical, the horizontal, and the crossed slices obtained in one detection process of 3D GPR, a 3D GPR cavity intelligent recognition model based on model-based transfer learning is proposed. Firstly, a large amount of 3D GPR data of urban road models with cavities are obtained through forwarding simulation. And the intelligent recognition model was pre-trained on the cavity detection data on three types of slices respectively. Then, through model-based transfer learning, a small amount of real underground cavity data is used to speed up the convergence speed of model training and optimize the structural parameters. It breaks through the limitation of the insufficient number of cavity samples for 3D radar detection on the intelligent learning model training, reduces algorithm training costs, and improves identification accuracy.

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Section: Research On Algorithmmentioning

confidence: 99%