Abstract: Damages in the anticorrosion coating of primary heating pipeline networks exhibit multiscale irregular morphological features, such as pointlike corrosion holes and blocklike peeling areas. Conventional AI recognition networks employing a singlescale fail to identify these complex defect features, leading to reduced recognition accuracy.Therefore, a visual AI recognition method based on variant attention was proposed for detecting anticorrosion coating damage in primary heating pipeline networks. Firstly, mixed openingclosing morphological operations were applied to reconstruct images of the primary heating pipeline network. These operations were combined with Laplace operator sharpening to enhance image edges, differentiate target objects from the background, and adjust image amplitude information. Subsequently, the watershed algorithm was utilized for coarse segmentation of the images. To achieve more precise differentiation between damaged and intact areas of the anticorrosion coating, the multi-scale residual module (MSRM) region merging method was employed for fine segmentation. The attention mechanism of the multi-scale dilated attention UNet (MD-AU-Net) was improved to develop a variant attention mechanism. A variant attentionbased MD-AU-Net was adopted. Through prior information integration and dense connection strategies, it enabled more comprehensive extraction of multiscale irregular morphological features, thereby achieving visual AI recognition of damages in the anticorrosion coating of primary heating pipeline networks. Experimental results indicate that the proposed method achieves a miss rate of 0.02 for irregular damages in the anticorrosion coating of primary heating pipeline networks. This significantly improves recognition accuracy and is of great significance for ensuring the longterm safe operation of heating pipeline networks.