基于可分离卷积与小波变换融合的道路裂缝检测

Road Crack Detection Based on Separable Convolution and Wave Transform Fusion

针对目前对细小裂缝检测能力不强、分割精度低等问题,提出了一种改进的U-Net模型来检测路面裂缝,提高检测能力和分割精度。中文设计了新的模块MSDWBlock(Multi-Scale Depthwise Separable Convolutional Block),应用在编码器和解码器部分,通过深度可分离卷积增强模型的能力,扩大模型感受野,在跳跃连接部分引入了C2G注意力机制模块,提升模型对裂缝特征的感知能力;并引入了ASPP(Atrous Spatial Pyramid Pooling)和DWT(Discrete Wavelet Transformation)。ASPP通过在多个尺度上进行操作,有助于捕捉到裂缝的特征,而DWT能够减少卷积池化过程中的裂缝空间信息损失,保留裂缝边缘信息。这种结构设计使得网络更专注于裂缝的特征,从而提升了裂缝检测的准确性。通过实验证明所提模型显示出优于U-Net,Segnet,U2net等先进模型的精确性。在CFD数据集上mIoU,F1分别达到78.51%,0.868。这些成果表明,所提方法能有效提升道路裂缝检测的性能。

Aiming at the current problems of weak detection ability and low segmentation accuracy for small cracks,an improved U-Net model is proposed to detect road cracks and improve detection ability and segmentation accuracy.This paper designs a new module,multi scale depth separated convolutional block(MSDWBlock),which is applied in the encoder and decoder sections.Through its depthwise separable convolution,the model’s ability is enhanced,the model’s receptive field is expanded,and a C2G attention mechanism module is introduced in the skip connection section to enhance the model’s perception of crack features.And atrous spatial pyramid pooling(ASPP)and discrete wavelet transformation(DWT)are introduced.ASPP helps to capture the characteristics of cracks by operating at multiple scales,while DWT can reduce the loss of crack spatial information during convolutional pooling and preserve crack edge information.This structural design makes the network more focused on the characteristics of cracks,thereby improving the accuracy of crack detection.It has been demonstrated through experiments that the accuracy of the proposed model is better than that of advanced models such as U-Net,Segnet,and U2net.On the CFD dataset,mIoU and F1 reaches 78.51%and 0.868 respectively.These results indicate that the proposed method can effectively improve the perfor-mance of road crack detection.