This paper presents a comparative study of semantic segmentation methods for the automated detection of structural damage in images. The goal is to identify models that offer an optimal balance between accuracy and computational efficiency, enabling practical applications in areas such as building inspection and infrastructure maintenance. Using the DACL10k dataset, which contains approximately 8,000 annotated images with 19 damage classes (e.g. cracks, corrosion, spalling), we evaluate a YOLO11-based segmentation method against encoder-decoder architectures, specifically DeepLabV3+ and UNet++, combined with different encoders such as ResNet, EfficientNet, MobileNet and the Mix Vision Transformer. These architectures are evaluated using standard segmentation metrics, such as mean Intersection over Union (mIoU), Precision, Recall and F1 Score. Our results show that DeepLabV3+ in combination with a transformer-based encoder achieves the highest mIoU of 0.409, significantly outperforming the YOLO11 model (mIoU = 0.321). These findings emphasize the limitations of object detection models such as YOLO for pixel-level segmentation tasks and highlight the potential of attention-based architectures for accurate and efficient structural damage analysis.