Segmentation network of concrete cracks with multi-frequency OctaveRes dual encoder and cross-attention mechanism optimized by average weight

Concrete cracks are one of the most harmful flaws on the road, threatening traffic safety. In this paper, an effective crack segmentation network MOACA-CrackNet that strives to boost both the model generalization rate and segmentation accuracy of crack segmentation is proposed to segment various types of cracks rapidly and accurately in a variety of acquisition conditions. First, a multi-frequency OctaveRes dual encoder is designed to reduce spatial redundancy by sharing information from neighboring locations. Then, an average weight cross-attention mechanism is designed to suppress redundant background information and improve information exchange between frequencies. Finally, depthwise separable convolution is used to reduce the number of parameters. A dataset with a total of 2062 crack images is constructed in this research, MOACA-CrackNet is trained and tested on this dataset. The experimental results show that MOACA-CrackNet has a good segmentation performance for tiny cracks, the F1-score and mIoU reached 89.2% and 91.32%, respectively.