The efficient identification of microcracks is of great significance to the early fault diagnosis of structures. The edge pixels of the microcracks have obvious changes that can be regarded as a salient object in the image for detection. Aiming at the differences in microcracks features at different scales of the encoder network, we proposed an embedded U-Net model that combines a cross-stage feature fusion module (CSF) and a layerwise feature fusion module (LWF). The embedded U-Net model can be used to enhance the spatial and edge information of high-resolution images and refine the extraction of low-resolution and high-dimensional channel information. In the low-scale saliency map produced by the decoder network, the bilinear interpolation method easily blurs and distorts the edges of the microcracks. To solve this problem, we send the low-quality saliency map into the subpixel super-resolution reconstruction module (SPR) to reconstruct high-quality pixel information. The SPR module is used to learn the low-resolution pixel information through the convolution operation, and the high-resolution pixels information can be obtained by the reorganization of learned pixels. A large number of experiments indicate that the 2_CSF+4_LWF+SPR has the best model performance. Its F1 score is 0.709, MAE is 0.0074, and the model parameter is only 55.6 MB. It can be seen from the qualitative analysis that LWF can improve the detection performance of microcrack ends and fuzzy cracks, and the SPR can refine the microcrack edges in the saliency map and reduce some interference from background impurities.