深度神经网络和计算机视觉技术近年来在结构健康监测中发挥了越来越重要的作用。利用无人机航拍采集的桥梁斜拉索损伤图像数据,研究基于深度学习技术的斜拉索PE护套损伤识别方法。为实现在较低运算能力设备上对大跨度桥梁斜拉索表面局部损伤的智能快速识别，解决传统深度卷积神经网络的运算效率相对较低、模型参数规模较大的问题，提出轻量化处理的区域推荐型卷积神经网络模型。介绍区域推荐网络与其轻量化改进方法的理论基础，分析轻量化模型处理的必要性，其能在保证识别精度的前提下降低模型训练与预测的设备性能需求，达到节约计算资源与时间的目的；通过数据增广等多手段解决损伤样本数据量不足的问题，设置对比试验，统计分析结果，验证了轻量化神经网络模型的优越性。结果表明，轻量化网络在牺牲少量识别准确度的前提下，能够在较大程度上实现对模型复杂度与计算量的改进，在工程应用中能有效拓展神经网络的实用性。

In recent years, deep neural network and computer vision techniques have played an increasingly important role in structural health monitoring. In this paper, deep learning technology is used to identify the PE sheath damage through the image data of bridge stay cable damage collected by UAV. This paper aims to realize the intelligent and rapid identification of local damage on the surface of stay cables of long-span bridges on devices with low computational ability, and to solve the problems of relatively low computational efficiency and large scale of model parameters in the current research of traditional deep convolution neural network. A lightweight region proposal convolution neural network model is proposed. Firstly, the theoretical basis of region proposal network and its lightweight improvement method is introduced, and the necessity of lightweight model processing is analyzed. It reduces the performance requirements of devices for model training and prediction under the premise of ensuring identification accuracy, achieving the purpose of saving computational resources and time. Secondly, the problem of insufficient data of damage samples is solved by multiple means of data augmentation. The contrast experiment and the analysis of statistical results verify the superiority of the lightweight neural network model. The results show that the lightweight network can improve the complexity and quantity of calculation of the model to a large extent under the premise of a small sacrifice of recognition accuracy. It effectively expands the practicability of the neural network in engineering applications.

TU997

国家重点研发计划（2021YFF0501003）