Preservation of Cultural Heritage Buildings in Central Plains via Novel Detection Approach

Abstract

Modern nations recognize cultural heritage as an expression of culture and variety. Conserving and repurposing historic buildings has only been more popular in the last decades. Nonetheless, a considerable portion of cultural legacy is afflicted by structural concerns that endanger the safety of buildings and people. Challenges include a scarcity of resources, whether financial, human, or material. This initiative aims to employ deep learning (DL) approaches to preserve cultural heritage buildings, particularly in poor nations where these buildings are still being maintained. To overcome these issues this study proposed a novel Flower Pollination improved Resnet (FP-IResNet) to detect preservation of cultural heritage buildings. The image data were collected from China's cultural heritage buildings. The data is preprocessed using normalization. Histogram of Oriented Gradients (HOG) using extract the features for preprocessed data. The proposed method is implemented using Python software. The findings reveal that the suggested obtained greater performance in the detection of cultural heritage buildings than other traditional algorithms. The suggested concept enables the computerized preservation of cultural heritage buildings, leading to improved accuracy and reduced individual fault. Performance measures show that the model is successful in correctly categorizing and detecting heritage buildings that require preservation, with high accuracy (96.82%), precision (97.21%), recall (97.58%), and an F1 score (93.58%). The research emphasizes how computerized techniques could enhance the precision and effectiveness of CH conservation initiatives