Research on Crack Detection Algorithm of Tunnel Lining Based on Improved Cuckoo Search Optimization Deep Convolutional Network
In response to the issue of low accuracy in tunnel lining crack detection algorithms,this paper proposes an improved cuckoo search optimization deep convolutional network algorithm for detecting tunnel lining crack images.First,based on the EfficientNet convolutional block stacking network and using the Mobile Inverted Residual Block(MBConv)with depthwise separable convolution,the algorithm efficiently extracts semantic features of crack images at multiple scales.Additionally,an improved Convolutional Block Attention Module(CBAM)is introduced to enhance the impact of key features.To prevent the loss of edge detail features,a Boundary Enhancement Module(BEM)is utilized to adjust the weight of boundary position feature details.Finally,a roulette wheel improved adaptive Cuckoo search is used to optimize segmentation threshold θ,resulting in a lining crack image detection algorithm.Ablation experiment results indicate that various optimized improvement modules can effectively enhance the performance of the algorithm model,achieving accuracy rates of 95.74％and 97.26％under interference and non-interference conditions,respectively.Compared to other algorithms,the accuracy rate of crack detection for this algorithm model reaches 94.91％,which is superior to algorithms such as Mask R-CNN and DeepLabv3.
lining crackstunnel structuredetection algorithmimage features
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