To address the typical challenges in visible and infrared image fusion, such as difficulty in recognizing small cracks, loss of texture details, and introduction of edge artifacts due to the simultaneous weakening of light intensity, this study proposes a multiscale feature extraction–multiscale attention generative adversarial network (M2GAN) method for image fusion. First, the M2GAN introduces a multiscale feature-extraction module that utilizes aligned visible and infrared images to extract information at different scales from both image types. This approach ensures that crack details and semantic information are preserved during the fusion process through side connections, thus resulting in more prominent crack features. Additionally, a multiplexed attention mechanism is proposed to stitch the multiscale fused image with the infrared and visible source images to construct the infrared intensity path and visible gradient path, respectively, thus preserving more target and background information. On a custom-developed dataset, the results of six evaluation indices show significant improvements by the proposed method compared with many mainstream image-fusion algorithms. Specifically, the structural similarity and edge retention improved by an average of 10.66% and 24.92%, respectively. The M2GAN demonstrates better visual effects and structural similarity, thus outperforming comparative methods in objective evaluations.