Dual flow fusion model for concrete surface crack segmentation

TL;DR

This paper introduces a dual stream fusion model for concrete crack segmentation that enhances accuracy and robustness by combining long-distance and local feature extraction with an interaction fusion mechanism and edge optimization.

Contribution

It proposes a novel dual-stream fusion architecture with an interaction mechanism and edge optimization for improved crack segmentation in complex backgrounds.

Findings

F1 score of 93.7% on DeepCrack dataset

IOU of 86.6% on DeepCrack dataset

F1 score of 78.1% on CRACK500 dataset

Abstract

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%.