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Crack is the external expression form of potential safety risks in bridge construction. Currently, automatic detection and segmentation of bridge cracks remains the top priority of civil engineers. With the development of image segmentation techniques based on convolutional neural networks, new opportunities emerge in bridge crack detection. Traditional bridge crack detection methods are vulnerable to complex background and small cracks, which is difficult to achieve effective segmentation. This study presents a bridge crack segmentation method based on a densely connected U-Net network (BC-DUnet) with a background elimination module and cross-attention mechanism. First, a dense connected feature extraction model (DCFEM) integrating the advantages of DenseNet is proposed, which can effectively enhance the main feature information of small cracks. Second, the background elimination module (BEM) is proposed, which can filter the excess information by assigning different weights to retain the main feature information of the crack. Finally, a cross-attention mechanism (CAM) is proposed to enhance the capture of long-term dependent information and further improve the pixel-level representation of the model. Finally, 98.18% of the Pixel Accuracy was obtained by comparing experiments with traditional networks such as FCN and Unet, and the IOU value was increased by 14.12% and 4.04% over FCN and Unet, respectively. In our non-traditional networks such as HU-ResNet and F U N-4s, SAM-DUnet has better and higher accuracy and generalization is not prone to overfitting. The BC-DUnet network proposed here can eliminate the influence of complex background on the segmentation accuracy of bridge cracks, improve the detection efficiency of bridge cracks, reduce the detection cost, and have practical application value.

A simple and controllable method was reported for the decoration of CuO nanoplate arrays with Au nanoparticles. It had been achieved through the reaction between Sn 2? and AuCl 4 -in the presence of CuO nanoplate arrays. The structure and electron field emission properties of CuO nanoplate arrays decorated with different amounts of Au nanoparticles were investigated. The results demonstrated a remarkable enhancement of field emission performance of CuO nanoplate arrays decorated with Au nanoparticles. The effect of Au amount on the field emission performance was studied in detail, and excellent field emission properties such as a low turn-on electric field of 6.7 V/lm and a high field enhancement factor of 516 could be realized from the optimized sample. On the basis of experimental results, a possible mechanism for the formation of the CuO nanoplate arrays decorated with Au nanoparticles was speculated.

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