Applying optimized YOLOv8 for heritage conservation: enhanced object detection in Jiangnan traditional private gardens

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.