基于轻量化神经网络的石窟壁画破损检测方法OA北大核心CSTPCD

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％.