Applying optimized YOLOv8 for heritage conservation: enhanced object detection in Jiangnan traditional private gardens
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Abstract
Abstract This study aims to promote the protection and inheritance of cultural heritage in private gardens in the Jiangnan area of China. By establishing a precise visual labeling system and accelerating the construction of a database for private garden features, we deepen the understanding of garden design philosophy. To this end, we propose an improved Jiangnan private garden recognition model based on You Only Look Once (YOLO) v8. This model is particularly suitable for processing garden environments with characteristics such as single or complex structures, rich depth of field, and cluttered targets, effectively enhancing the accuracy and efficiency of object recognition. This design integrates the Diverse Branch Block (DBB), Bidirectional Feature Pyramid Network (BiFPN), and Dynamic Head modules (DyHead) to optimize model accuracy, feature fusion, and object detection representational capability, respectively. The enhancements elevated the model's accuracy by 8.7%, achieving a mean average precision (mAP@0.5) value of 57.1%. A specialized dataset, comprising 4890 images and encapsulating various angles and lighting conditions of Jiangnan private gardens, was constructed to realize this. Following manual annotation and the application of diverse data augmentation strategies, the dataset bolsters the generalization and robustness of the model. Experimental outcomes reveal that, compared to its predecessor, the improved model has witnessed increments of 15.16%, 3.25%, and 11.88% in precision, mAP0.5, and mAP0.5:0.95 metrics, respectively, demonstrating exemplary performance in the accuracy and real-time recognition of garden target elements. This research not only furnishes robust technical support for the digitization and intelligent research of Jiangnan private gardens but also provides a potent methodological reference for object detection and classification research in analogous domains.
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In cultural heritage, YOLO is employed to detect and classify artifacts in archaeological studies, aiding in the preservation of cultural history and restoration of structural defects in heritage sites.
“In cultural heritage, YOLO is employed to detect and classify artifacts in archaeological studies, aiding in the preservation of cultural history and restoration of structural defects in heritage sites [43,44].”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [43]
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- Authors
- Gao, C., Zhang, Q., Tan, Z., Zhao, G., Gao, S., Kim, E., & Shen, T.
- Affiliations
- Japan Advanced Institute of Science and Technology (JP)
- Northeast Agricultural University (CN)
- Tongji University (CN)
- Corresponding author
- Zhang, Qingzhu
- Kim, Eunyoung
- Venue
- Heritage Science
- Published
- 2024-01-29
- Volume
- 12
- Issue
- 1
- ISSN
- 2050-7445 (ISSN-L)
- OpenAlex
- W4391317385
- Cited by
- 39
- References
- 25
- Citations by year
- 2026: 9 · 2025: 18 · 2024: 12
- FWCI
- 5.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Heritage Science (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Scientific Research Fund of Zhejiang Provincial Education Department (Y202351058)
- Chinese Ministry of Education Humanities and Social Sciences Research Youth Fund Project (23YJC760101)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- cultural heritage preservation, object detection, YOLOv8, cultural heritage digitization, data augmentation, BiFPN, dynamic head
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