计算机与数字工程2025，Vol.53Issue(12)：3299-3304,3312,7.DOI:10.3969/j.issn.1672-9722.2025.12.001
全局特征融合双层轻量化的裂缝样本扩充算法
Global Feature Fusion and Double-layer Lightweight Crack Sample Augmentation Algorithm
摘要
Abstract
As a common problem of small sample identification,tunnel crack detection based on deep learning will cause low classification accuracy due to too few crack samples.Based on the CycleGAN model,a crack sample augmentation method GDCycle-GAN based on the global feature fusion double-layer lightweight model is proposed.The GFF(Global Featue Fusion)module is in-troduced into the generator of CycleGAN,which integrates the semantic information of the upper and lower layers of the crack image to obtain multi-scale information,and uses the attention mechanism to strengthen the selection of the crack image feature informa-tion to weaken the crack background information.Aiming at the problem that the efficiency of sample generation is not high,the DL(Double Lightweight)module is introduced into the discriminator of CycleGAN,and group convolution and deep separable convolu-tion are used instead of the original convolutional layer to reduce the amount of convolution operation parameters of each layer and improve the quality of generated samples and the speed of network training.Experimental results show that after the small sample ex-pansion of the proposed algorithm,the speed of expanded sample training and the subsequent crack classification accuracy are im-proved.关键词
深度学习/裂缝检测/特征融合/注意力机制/GDCycleGAN模型Key words
deep learning/crack detection/feature fusion/attention mechanism/GDCycleGAN model分类
信息技术与安全科学引用本文复制引用
谢永华,李舰远,陈雅,卓安南..全局特征融合双层轻量化的裂缝样本扩充算法[J].计算机与数字工程,2025,53(12):3299-3304,3312,7.基金项目
国家自然科学基金项目(编号:62076123)资助. （编号:62076123）
