SmaAt-UNet Optimized by Particle Swarm Optimization (PSO): A Study on the Identification of Detachment Diseases in Ming Dynasty Temple Mural Paintings in North China

Luo, Chuanwen; Shang, Zikun; Zhang, Yan; Pan, Hao; Nuermaimaiti, Abdusalam; Wang, Chenlong; Li, Ning; Zhang, Bo

doi:10.3390/app152212295

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SmaAt-UNet Optimized by Particle Swarm Optimization (PSO): A Study on the Identification of Detachment Diseases in Ming Dynasty Temple Mural Paintings in North China

by

Chuanwen Luo

^1,*

,

Zikun Shang

¹,

Yan Zhang

²,

Hao Pan

¹,

Abdusalam Nuermaimaiti

¹,

Chenlong Wang

¹,

Ning Li

² and

Bo Zhang

^1,*

¹

Department of Architecture, School of Architecture and Art, North China University of Technology, Jinyuanzhuang Road 5, Shijingshan District, Beijing 100144, China

²

Beijing Historical Building Protection Engineering Technology Research Center, Beijing University of Technology, Beijing 100124, China

^*

Authors to whom correspondence should be addressed.

Appl. Sci. 2025, 15(22), 12295; https://doi.org/10.3390/app152212295

Submission received: 26 September 2025 / Revised: 17 November 2025 / Accepted: 17 November 2025 / Published: 19 November 2025

(This article belongs to the Special Issue AI-Driven Computer Vision and Pattern Recognition: Challenges and Applications)

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Abstract

The temple mural paintings of the Ming Dynasty in China are highly valuable cultural heritage. However, murals in North China have long faced deterioration such as pigment-layer detachment, which seriously threatens their preservation and study, gradually leading to cultural incompleteness and impeding protection decisions. This study proposes a coherent deep-learning technical paradigm, constructs a mural dataset, compares the performance of multiple models, and optimizes the selected model to enable automatic identification of mural detachment. The study applies five segmentation models—UNet, U2-NetP, SegNet, NestedUNet, and SmaAt-UNet—to perform a systematic comparison under the same conditions on 37,685 image slices, and evaluates their performance using four metrics: IoU, Dice, MAE, and mPA. Owing to its lightweight structure and attention-enhanced feature-extraction module, SmaAt-UNet effectively preserves mural edge details and performs best at identifying pigment-layer detachment. After introducing Particle Swarm Optimization (PSO), the IoU of the SmaAt-UNet model on the dataset increased to 73.25%, the Dice increased to 79.36%, the mPA increased to 97.02%, and the MAE decreased from 0.0592 to 0.0455, corresponding to an absolute reduction of 0.0137, and the model’s generalization ability and edge-recognition accuracy were significantly enhanced. This study constructs a systematic identification framework for pigment layer detachment in Ming Dynasty (1368–1644 AD) temple murals, closely combining deep learning technology with cultural heritage protection. It not only realizes the automatic identification of disease areas but also provides technical support for preventive protection and the construction of digital archives.

Keywords: Ming Dynasty mural paintings; detection of pigment layer detachment; SmaAt-UNet; PSO; cultural heritage protection

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MDPI and ACS Style

Luo, C.; Shang, Z.; Zhang, Y.; Pan, H.; Nuermaimaiti, A.; Wang, C.; Li, N.; Zhang, B. SmaAt-UNet Optimized by Particle Swarm Optimization (PSO): A Study on the Identification of Detachment Diseases in Ming Dynasty Temple Mural Paintings in North China. Appl. Sci. 2025, 15, 12295. https://doi.org/10.3390/app152212295

AMA Style

Luo C, Shang Z, Zhang Y, Pan H, Nuermaimaiti A, Wang C, Li N, Zhang B. SmaAt-UNet Optimized by Particle Swarm Optimization (PSO): A Study on the Identification of Detachment Diseases in Ming Dynasty Temple Mural Paintings in North China. Applied Sciences. 2025; 15(22):12295. https://doi.org/10.3390/app152212295

Chicago/Turabian Style

Luo, Chuanwen, Zikun Shang, Yan Zhang, Hao Pan, Abdusalam Nuermaimaiti, Chenlong Wang, Ning Li, and Bo Zhang. 2025. "SmaAt-UNet Optimized by Particle Swarm Optimization (PSO): A Study on the Identification of Detachment Diseases in Ming Dynasty Temple Mural Paintings in North China" Applied Sciences 15, no. 22: 12295. https://doi.org/10.3390/app152212295

APA Style

Luo, C., Shang, Z., Zhang, Y., Pan, H., Nuermaimaiti, A., Wang, C., Li, N., & Zhang, B. (2025). SmaAt-UNet Optimized by Particle Swarm Optimization (PSO): A Study on the Identification of Detachment Diseases in Ming Dynasty Temple Mural Paintings in North China. Applied Sciences, 15(22), 12295. https://doi.org/10.3390/app152212295

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SmaAt-UNet Optimized by Particle Swarm Optimization (PSO): A Study on the Identification of Detachment Diseases in Ming Dynasty Temple Mural Paintings in North China

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