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The existence of cracks and other damages pose a significant threat to thesafe operation of transportation infrastructure. Traditional manual detectionand ultrasound equipment testing consume a lot of time and resources. With thedevelopment of deep learning technology, many deep learning models have beenwidely applied to practical visual segmentation tasks. The detection methodbased on deep learning models has the advantages of high detection accuracy,fast detection speed, and simple operation. However, deep learning-based cracksegmentation models are sensitive to background noise, have rough edges, andlack robustness. Therefore, this paper proposes a crack segmentation modelbased on the fusion of dual streams. The image is inputted simultaneously intotwo designed processing streams to independently extract long-distancedependence and local detail features. The adaptive prediction is achievedthrough the dual-headed mechanism. Meanwhile, a novel interaction fusionmechanism is proposed to guide the complementary of different feature layers toachieve crack location and recognition in complex backgrounds. Finally, an edgeoptimization 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 ofsegmentation results on the CRACK500[2] dataset is 78.1%, and the IOU value is66.0%.

Dual flow fusion model for concrete surface crack segmentation