The existence of cracks and other damages pose a significant threat to the safe operation of transportation infrastructure. Traditional manual detection and ultrasound equipment testing consume a lot of time and resources. With the development of deep learning technology, many deep learning models have been widely applied to practical visual segmentation tasks. The detection method based on deep learning models has the advantages of high detection accuracy, fast detection speed, and simple operation. However, deep learning-based crack segmentation models are sensitive to background noise, have rough edges, and lack robustness. Therefore, this paper proposes a crack segmentation model based on the fusion of dual streams. The image is inputted simultaneously into two designed processing streams to independently extract long-distance dependence and local detail features. The adaptive prediction is achieved through the dual-headed mechanism. Meanwhile, a novel interaction fusion mechanism is proposed to guide the complementary of different feature layers to achieve crack location and recognition in complex backgrounds. Finally, an edge optimization method is proposed to improve the accuracy of segmentation. Experiments show that the F1 value of segmentation results on the DeepCrack[1] public dataset is 93.7% and the IOU value is 86.6%. The F1 value of segmentation results on the CRACK500[2] dataset is 78.1%, and the IOU value is 66.0%.

翻译：裂缝等损伤的存在对交通基础设施的安全运行构成重大威胁。传统的人工检测和超声波设备检测消耗大量时间和资源。随着深度学习技术的发展，许多深度学习模型已被广泛应用于实际的视觉分割任务。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模型对背景噪声敏感、边缘粗糙且缺乏鲁棒性。因此，本文提出一种基于双流融合的裂缝分割模型。将图像同时输入两个设计的处理流，分别独立提取长距离依赖和局部细节特征。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，用于指导不同特征层的互补，以实现复杂背景下的裂缝定位与识别。最后，提出一种边缘优化方法以提高分割精度。实验表明，在DeepCrack[1]公开数据集上，分割结果的F1值为93.7%，IOU值为86.6%；在CRACK500[2]数据集上，分割结果的F1值为78.1%，IOU值为66.0%。