  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%.
