Most of the existing bridge crack image inpainting methods are single target restoration,which cannot generate multiple reasonable filling contents based on valid information around the hole. Moreover,the inpainting results suffer from structural distortion and texture blurring.A diversity crack image inpainting network based on the mask distance convolutional block attention module (MD-CBAM) is proposed in this paper,which mainly consists of a diversity structure generator and a texture generator.The regional structure attention is proposed to reduce the difference between the pixels in the masked region and the valid pixels,and the average pooling is performed on the attention scores according to the mask features to improve the inference ability of the model to the masked area.The MD-CBAM module is designed to synthesize high-quality features in the texture generation stage.The module utilizes the distance information between features and semantic information to effectively enhance the capability of the model to fill large holes.The experimental results show that the inpainted image has a more definite structure and a more reasonable texture,and the peak signal-to-noise ratio (PSNR) and Fréchet inception distance (FID) reach the best at each mask ratio, where the PSNR increases by 0.22—2.38 dB at the mask ratio of ［0.4,0.5) and the structural similarity (SSIM) value is optimal.