关键词: 沥青路面图像, 裂缝分割, 轻量化神经网络, 倒金字塔结构, 并行空洞卷积

Abstract: 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(PIPNet) based on U-Net encoder-decoder structure was proposed in this paper. 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 network, an inverse residual lightweight module was designed, which embedded with parallel dual pooling attention. Testing results on GAPS384 dataset show that the proposed lightweight segmentation method improves its mIOU value by 1.10 percentage points, respectively when the Params and MFLOPs values are only about one-sixth of the ResNet50 encoding. The mIOU value is 4.14 and 9.95 percentage points higher than the lightweight GhostNet and SegNet networks, respectively. Experimental results show that the proposed method has high crack segmentation performance while reducing the model complexity, and has good adaptability to segmentation of different road crack images.

Key words: asphalt pavement image, crack segmentation, lightweight neural network, inverted pyramid structure, parallel dilated convolution