An automatic detection method of the mural shedding disease using YOLOv4

Hu, Chunmei; Dong, Yuxin; Xia, Guofang; Liu, Xi

doi:10.1117/12.2625707

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…The migration learning-based center-of-mass positioning method can effectively solve the above problems, with higher accuracy and timeliness. Migration learning adopts the dataset of similar domains to pre-train the segmentation model, which can overcome the shortcomings of model overfitting caused by insufficient data of cultural relics images [19]. At the same time, the center-of-mass in different regions is weighted using a deep feature extraction network structure, which can alleviate the problems of irregular shape and subjective labelling to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning-Based Center-of-Mass Positioning Methods for Cultural Relics

He,

Wei,

Gong

et al. 2024

IEEE Access

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In order to address the problem of incomplete data set of real seismic impact of cultural relics in collections, we propose a small sample set oriented center-of-mass positioning method to solve the centerof-mass attribute of cultural relics ontology. First, the method combines the cultural relics image data to construct a deep feature fusion cultural relics ontology recognition model, which extracts and learns the cultural relics image features through multi-layer feature extraction, global information perception and ontology fusion recognition, so as to realize the efficient and accurate recognition of the cultural relic's ontology. Then, the parameter synchronous migration learning method is designed to train and initialize the model, and the data migration strategy of semi-supervised learning is used to retrain the model using the public dataset and the cultural relic image data, to achieve the fine-tuning of the model parameters to improve the accuracy of the cultural relic ontology recognition. Finally, the center-of-mass positioning method is designed to integrate the attention mechanism with the quality of relics and other ontological attributes, and the segmentation of the relic's ontology region is achieved by rectangular stereo fitting and the calculation of the center-of-mass points. At the same time, the fusion self-attention mechanism is used to adjust the regional center-of-mass weights to achieve weighted positioning of the center-of-mass position of the cultural relics. The experimental results show that the present method achieves the best Dice, Acc and MIoU metrics in comparison with various classical models, with improvements of 8.3%, 5.1% and 3.4% respectively. The overall center-of-mass offset of cultural relics is less than 2%, which can achieve accurate identification and center-of-mass positioning of the cultural relics body, fully improve the seismic impact dataset of cultural relics, and enhance the ability of preventive protection of cultural relics against earthquakes.

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Section: Introductionmentioning

confidence: 99%

Transfer Learning-Based Center-of-Mass Positioning Methods for Cultural Relics

He,

Wei,

Gong

et al. 2024

IEEE Access

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“…In terms of deep learning, some scholars have achieved rapid mural deterioration detection through multi-path CNN(convolutional neural network) [5]. In 2021, Hu used YOLOv4 algorithm to train the data set, so as to realize the automatic and rapid identification of diseases in the orthophoto of murals [6]. However, deep learning requires a large number of training sample data,The diseases of painted cultural relics are different, and they do not have good training conditions.…”

mentioning

confidence: 99%

A high-precision automatic extraction method for shedding diseases of painted cultural relics based on three-dimensional fine color model

Hu,

Huang,

XIA

et al. 2024

Preprint

Self Cite

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In recent years, with the development of three-dimensional digitization of cultural relics, most cultural relic protection units have a large number of fine three-dimensional data of cultural relics, especially complex geometric objects such as painted cultural relics. At present, how to automatically extract surface disease information from the fine three-dimensional color model of painted cultural relics and avoid the accuracy loss caused by reducing the dimension by conventional methods is an urgent problem to be solved in the investigation of cultural relics diseases.In view of the above problems, this paper proposes an automatic and high-precision extraction method for cultural relics surface shedding diseases based on three-dimensional fine data. Firstly, this paper designs a two-dimensional and three-dimensional integrated data conversion model based on OSG three-dimensional engine, which realizes the mutual conversion between three-dimensional color model texture and two-dimensional image. Secondly, a SLIC segmentation algorithm with adaptive K value is proposed, which solves the problem of superpixel K value setting and improves the accuracy of image segmentation. Finally, through the two-dimensional and three-dimensional integrated model, the disease is statistically analyzed and labeled on the three-dimensional model.Experiments show that for painted plastic objects with complex surfaces, the disease extraction method based on three-dimensional fine model proposed in this paper has improved geometric accuracy compared with the current popular orthophoto extraction method, and the disease investigation is more comprehensive ; compared with the current three-dimensional manual extraction method in commercial software, this method greatly improves the efficiency of disease extraction while ensuring the extraction accuracy. The research method of this paper activates a large number of existing three-dimensional fine data of cultural protection units, and converts data mining and analysis from conventional two-dimensional data to three-dimensional data, which is more in line with the scientific utilization of data in accuracy and efficiency, and has certain scientific research value, leading value and practical significance.

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A high-precision automatic extraction method for shedding diseases of painted cultural relics based on three-dimensional fine color model

Hu,

Huang,

Xia

et al. 2024

Herit Sci

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In recent years, with the development of 3D digitization of cultural relics, most cultural sites contain a large number of fine 3D data of cultural relics, especially complex geometric objects such as painted cultural relics. At present, how to automatically extract surface damage information from the fine 3D color model of painted cultural relics and avoid the loss of accuracy caused by reducing the dimension using conventional methods is an urgentproblem. In view of the above issues, this paper proposes an automatic and high-precision extraction method for cultural relics surface shedding diseases based on 3D fine data. First, this paper designs a 2D and 3D integrated data conversion model based on OpenSceneGraph, a 3D engine, which performs mutual conversion between 3D color model textures and 2D images. Second, this paper proposes a simple linear iterative clustering segmentation algorithm with an adaptive k value, which solves the problem of setting the superpixel k value and improves the accuracy of image segmentation. Finally, through the 2D and 3D integrated models, the disease is statistically analyzed and labeled on the 3D model. Experiments show that for painted plastic objects with complex surfaces, the disease extraction method based on the 3D fine model proposed in this paper has improved geometric accuracy compared with the current popular orthophoto extraction method, and the disease investigation is more comprehensive. Compared with the current 3D manual extraction method in commercial software, this method greatly improves the efficiency of disease extraction while ensuring extraction accuracy. The research method of this paper activates many existing 3D fine data of cultural protection units and converts conventional 2D data mining and analysis into 3D, which is more in line with the scientific utilization of data in terms of accuracy and efficiency and has certain scientific research value, leading value and practical significance.

show abstract

An automatic detection method of the mural shedding disease using YOLOv4

Cited by 4 publications

References 5 publications

Transfer Learning-Based Center-of-Mass Positioning Methods for Cultural Relics

Transfer Learning-Based Center-of-Mass Positioning Methods for Cultural Relics

A high-precision automatic extraction method for shedding diseases of painted cultural relics based on three-dimensional fine color model

A high-precision automatic extraction method for shedding diseases of painted cultural relics based on three-dimensional fine color model

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