A Detection Method for Pavement Cracks Based on an Improved U-Shaped Network

Due to the traditional crack segmentation algorithm is difficult to identify narrow cracks and the segmentation edge is not accurate. This paper proposes a pavement crack detection method based on improved U-Shaped Network (Unet) to increase detection accuracy. Since traditional Unet is a type of"shallow"neural network, it is not good for extracting complex crack features. The Oxford University Visual Geometry Group Network (VGG16) is therefore used for feature extraction, in order to improve the accuracy of crack feature extraction. In addition, the fusion of high- and low-order features generate several useless features. The compression and excitation unit (SE block) is added to the decoding part of the model to develop a crack attention unit which allows the network to focus on the crack features under different channels. Moreover, an improved Unet is proposed by combining SE block with VGG16 (SE-VUnet). In addition, a transfer learning method is used to transfer the pre-trained VGG16 network weight on ImageNet for crack detection. By selecting the Crack500 data set and using the camera to collect images to develop1600 pavement crack data sets, the SE-VUnet model is trained again to obtain the crack segmentation results. The weighted harmonic mean F1 of Precision and Recall and Jaccard similarity coefficient are used as quantitative evaluation indicators. The segmentation effect and real-time performance of SE-VUnet are compared with Unet and three other representative models. Study results show that the comprehensive F1 and the Jaccard coefficient of SE-VUnet model is 0.840 3 and 0.722 1, which is 1.04% and 1.51% higher than Unet respectively, as well as other three comparison models. The time for the SE-VUnet to screen a single-frame image is 89 ms, which is only 5ms slower than the Unet but with a significant improvement over the crack segmentation and detection process.