轻量化沥青路面裂缝图像分割网络PIPNet

PIPNet:lightweight asphalt pavement crack image segmentation network

裂缝分割是对路面病害损坏程度评估的重要前提,为平衡深度神经网络分割的有效性与实时性,提出一种基于U-Net编码-解码结构的轻量化沥青路面裂缝图像分割网络PIPNet(Parallel dilated convolution of Inverted Pyramid Network)。编码部分为倒金字塔结构,提出了具有不同空洞率的多分支并行空洞卷积模块,结合深度可分离卷积和普通卷积,逐级减少并行卷积的个数,对表层、中层及底层特征提取多尺度信息并降低模型复杂度;同时借鉴GhostNet特点,设计了逆残差轻量化模块,嵌入并行双池化注意力。在GAPs384数据集上的测试结果表明,PIPNet在参数量(Params)和计算量(MFLOPs)仅为ResNet50编码近1/6的情况下,平均交并比(mIoU)提高了1.10个百分点,且较轻量化GhostNet和SegNet分别高出4.14与9.95个百分点。实验结果表明,PIPNet在降低模型复杂度的同时,有着较好的裂缝分割性能,且对不同路面裂缝图像分割适应性良好。

Crack segmentation is an important prerequisite for evaluating the damage degree of pavement diseases.In order to balance the effectiveness and real-time of deep neural network segmentation,a lightweight asphalt pavement crack segmentation neural network based on U-Net encoder-decoder structure was proposed,namely PIPNet(Parallel dilated convolution of Inverted Pyramid Network).The encoding part was an inverted pyramid structure.Multi-branch parallel dilated convolution module with different dilatation rates was proposed to extract multi-scale information from the top,middle and bottom features and reduce model complexity,which combined deep separable convolutions with ordinary convolutions and gradually reduced the number of parallel convolutions.Drawing on the characteristics of GhostNet,an inverse residual lightweight module was designed,which was embedded with parallel dual pooling attention.Test results on GAPs384 dataset show that,compared with ResNet50 encoding method,PIPNet has mIoU(mean Intersection over Union)1.10 percentage points higher with only about one-sixth of parameter quantity and MFLOPs(Million FLOating Point operations),and its mIoU is 4.14 and 9.95 percentage points higher than those of lightweight GhostNet and SegNet,respectively.Experimental results show that PIPNet has high crack segmentation performance while reducing the model complexity,and has good adaptability to segmentation of different pavement crack images.