Cracks and other diseases are important factors that threaten the safe operation of transportation infrastructure. Traditional manual detection and ultrasonic instrument detection consume a lot of time and resource costs. With the development of deep learning technology, many deep learning models are widely used in actual visual segmentation tasks. The detection method based on the deep learning model has the advantages of high detection accuracy, fast detection speed and simple operation. However, the crack segmentation based on deep learning has problems such as sensitivity to background noise, rough edges, and lack of robustness. Therefore, this paper proposes a fissure segmentation model based on two-stream fusion, which simultaneously inputs images into two designed processing streams to independently extract long-distance dependent and local detail features, and realizes adaptive prediction through a dual-head mechanism. At the same time, a new interactive fusion mechanism is proposed to guide the complementarity of different levels of features to realize the location and identification of cracks in complex backgrounds. Finally, we propose an edge optimization method to improve segmentation accuracy. Experiments have proved that the F1 value of the segmentation results on the DeepCrack[1] public dataset reached 93.7%, and the IOU value reached 86.6%; the F1 value of the segmentation results on the CRACK500[2] dataset reached 78.1%, and the IOU value reached 66.0%.

本文提出了一种基于双流融合的裂纹分割模型，具有更高的检测精度和更强的适应性，并提出了一种新的交互融合机制和边缘优化方法，以改善分割准确度。

混凝土表面裂缝分割的双流融合模型