信息与控制2024，Vol.53Issue(1)：108-119,12.DOI:10.13976/j.cnki.xk.2023.2456
基于轻量化神经网络的石窟壁画破损检测方法
Damage Detection Method for Grotto Murals Based on Lightweight Neural Network
摘要
Abstract
To address the issues of low detection precision and poor real-time performance in the process of grotto mural detachment and damage detection,we propose a grotto mural damage detection method based on a lightweight neural network and multiple attention mechanisms.First,Ghost Conv is introduced to complete lightweight feature extraction and reduce model complexity.Sec-ond,we add a double attention mechanism to increase the tendency of feature extraction and accel-erate model convergence.Finally,we use a weighted bidirectional feature pyramid network to effi-ciently fuse feature information and complete prediction by composite scaling.The experimental re-sults show that the improved algorithm reduces the number of network layers by 34.40％.The number of parameters and floating point operations are reduced by 62.98％and 68.77％,respec-tively,and the model volume is compressed by 62.78％.The detection precision is 64.7％,and the real-time detection speed is improved from 63.60 frame/s to 97.56 frame/s,which is approxi-mately 53.39％.关键词
深度学习/神经网络/轻量化模型/注意力机制/壁画破损检测Key words
deep learning/neural network/lightweight model/attention mechanism/murals damage detection分类
信息技术与安全科学引用本文复制引用
吴利刚,张梁..基于轻量化神经网络的石窟壁画破损检测方法[J].信息与控制,2024,53(1):108-119,12.基金项目
2021年度山西省哲学社会科学规划课题(2021YY198) （2021YY198）
2020年山西大同大学科学研究项目云冈专项(2020YGZX014) （2020YGZX014）
2021年山西省高等学校科技创新项目(20211391) （20211391）
2021年度校级科研专项项目(云冈学研究)(2021YGZX27) （云冈学研究）
