Ancient Architectural Damage Recognition Based on AlexNet and Transfer Learning

Liang Wang; Ke Ding

doi:10.54097/ajst.v7i2.12256

Authors

Liang Wang
Ke Ding

DOI:

https://doi.org/10.54097/ajst.v7i2.12256

Keywords:

Ancient architecture; Damage identification; Convolutional neural network; Transfer learning.

Abstract

Due to the need for a large dataset to train image recognition using convolutional neural networks, obtaining accurately categorized images of ancient architectural structure damage poses certain challenges. To improve the recognition accuracy of damage in ancient architectural structures, we have enhanced the Vgg-16 network by combining transfer learning techniques with convolutional neural networks. This design, a new image recognition method based on transfer learning, effectively achieves precise identification of structural damage in ancient architecture, showing potential for real-world applications.

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