面向石质文物裂隙的超声定位系统及算法研究

面向石质文物多年风化、干裂，急需修复的场景，针对深度学习在文物裂隙检测应用中的不足，提出一种基于缺陷特征增强和卷积神经网络(CNN)的石质文物缺陷定位算法。算法通过小波包分解进行特征提取并选择包含丰富特征信息的有效频段，作为CNN网络的输入，通过模型训练和波形分类识别，缩小定位范围，提高缺陷定位算法的泛化能力和识别率。本文搭建了以现场可编程门阵列(FPGA)为核心的超声检测平台，并在边长为40 cm的立方体试件上进行了实验验证，实验结果表明，波形识别准确率相较传统算法提高了11.3%,平均定位误差小于10%,为石质文物裂隙检测提供了可靠依据，有助于文物保护和修复。

Research on the ultrasonic localization system and algorithm for stone cultural relics

The stone cultural relics have been weathered and cracked for many years and are in urgent need of repair. To address this issue, presents a defect location algorithm for stone cultural relics based on defect feature enhancement and convolutional neural network (CNN) in view of the deficiency of depth learning in the application of crack detection for cultural relics. The algorithm extracts features through wavelet packet decomposition and selects an effective frequency band containing rich feature information as the input of the CNN network. Through model training and waveform classification recognition, it narrows the location range and improves the generalization ability and recognition rate of the crack location algorithm. An ultrasonic detection platform is established with field programmable gate array ( FPGA) as the core, and experimental verification is implemented on a cube specimen with a side length of 40 cm. The experimental results show that the accuracy rate of waveform identification is 11. 3% higher than that of traditional algorithms, and the average positioning error is less than 10% , which provides a reliable basis for defect detection of stone cultural relics and is helpful for the protection and restoration of cultural relics.