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%.

翻译：裂缝等病害是威胁交通基础设施安全运行的重要因素。传统的人工检测和超声波仪器检测消耗大量时间和资源成本。随着深度学习技术的发展，许多深度学习模型广泛应用于实际视觉分割任务。基于深度学习模型的检测方法具有检测精度高、检测速度快、操作简单等优点。然而，基于深度学习的裂缝分割存在对背景噪声敏感、边缘粗糙、鲁棒性不足等问题。为此，本文提出一种基于双流融合的裂缝分割模型，同时将图像输入两条设计的处理流，分别独立提取远程依赖特征和局部细节特征，并通过双头机制实现自适应预测。同时，提出一种新型交互融合机制，引导不同层级特征的互补性，实现复杂背景下裂缝的定位与识别。最后，提出一种边缘优化方法以提高分割精度。实验证明，在DeepCrack[1]公共数据集上的分割结果F1值达到93.7%，IOU值达到86.6%；在CRACK500[2]数据集上分割结果的F1值达到78.1%，IOU值达到66.0%。