# A Highway Pavement Crack Identification Method Based on an Improved U-Net Model
###### Abstract  
Crack identification plays a vital role in preventive maintenance strategies during highway pavement maintenance. Therefore, accurate identification of cracks in highway pavement images is the key to highway maintenance work. In this paper, an improved U-Net network adopting multi-scale feature prediction fusion and the improved parallel attention module was put forward to better identify concrete cracks. Multiscale feature prediction fusion combines multiple U-Net features generated by intermediate layers for aggregated prediction, thus using global information from different scales. The improved parallel attention module is used to process the U-Net decoded output of multi-scale feature prediction fusion, which can give more weight to the target region in the image and further capture the global contextual information of the image to improve the recognition accuracy. Improving the bottleneck layer is used to improve the robustness of the model and prevent overfitting. Experiments show that the improved U-Net network in this paper has a significant improvement over the original U-Net network. The performance of the proposed method in this paper was investigated on two publicly available datasets (Crack500 and CFD) and compared with competing methods proposed in the literature. Using the Crack500 dataset, the method in this paper achieved the highest score in precision (89.60%), recall (95.83%), mIOU (83.80%), and F1-score (92.61%). Similarly, for the CFD dataset, the method in this paper achieved high values for precision (93.29%), mIOU (82.07%), recall (86.26%), and F1-score (89.64%). Thus, the method has several advantages for identifying cracks in highway pavements and is an ideal tool for practical work. In future work, identifying more crack types and model light-weighting are the key objectives. Meanwhile, this paper provides a new idea for road crack identification. pavement crack; multi-scale feature prediction fusion; attention mechanism; U-Net; image segmentation +
Footnote †: journal: Medicaland other hazards that directly threaten the life and health of the nation. Therefore, it is necessary to identify pavement cracks in a timely manner. Highway pavement crack identification has been an important issue in the field of transportation construction [3]. Traditional pavement crack identification methods require a large number of manual inspections, which are time-consuming and labor-intensive, and the results are not accurate and stable enough. In order to solve this problem, more and more researchers have started to explore the use of deep learning techniques for automated pavement crack recognition. Before deep learning techniques were applied to pavement crack recognition, researchers usually used traditional image processing methods to identify cracks. Edge extraction operators [4; 5; 6] and threshold segmentation algorithms [7; 8] are the representatives of traditional pavement crack recognition. [PERSON] [9] proposed a crack feature segmentation algorithm based on adaptive threshold edge detection. However, in practical engineering, shadows, road marking lines, and stains on the road surface will affect the recognition accuracy of traditional methods. In addition to edge detection and threshold segmentation methods, there are crack recognition methods based on the wavelet transform. However, these methods have the disadvantages of high requirements for environmental factors and poor robustness [10; 11; 12]. In recent years, deep learning is increasingly used in the field of computer vision and is widely used in image processing, target detection, segmentation, and other fields due to its excellent feature learning capability and good classification effect. In pavement crack detection, deep learning can automatically learn feature representations in road images to effectively identify pavement cracks.