Identification and Classification of Bronze Surface Diseases Based on Improved YOLOv5

By leveraging the lightweight and high-performance characteristics of EfficientNetV2, this paper enhances the YOLOv5s model to accurately classify disease types in bronze wares while reducing human error. The efficiency of feature extraction across multiple scales is further improved through an optimized convolutional structure and a compound scaling method. The bronze wares housed in the Jingmen Museum serve as a case study to validate the model’s effectiveness. Photographs of the artifacts were captured using a Canon G1X Mark III digital camera and processed into a dataset comprising 1,037 images, labeled with defects such as surface flaws, holes, full-body mineralization, and cracks for training and validation. Results show that the enhanced YOLOv5s_EfficientNetV2 model significantly outperforms the original YOLOv5s model across key metrics including Precision, Recall, mAP@0.5, and mAP@0.5:0.95, with respective improvements of 7.0%, 1.7%, 2.2%, and 2.6%. Furthermore, a set of visual interfaces for disease detection in bronze wares has been developed using OpenCV and PyQt5, allowing intuitive visualization of the results. The study also explores the impact of batch size on model performance, revealing that a batch size of 8 offers the best trade-off between gradient estimation accuracy and training stability, thereby enhancing recognition capability. By quantifying the type and location of deterioration, this technology offers valuable data support for conservation strategies, provides robust assistance in the protection and restoration of bronze artifacts, and demonstrates strong potential for broader applications in cultural heritage preservation.