Accurate recognition of dangerous goods container signs is essential for maritime safety supervision, personnel protection, and logistics intelligence. Based on an enhanced YOLOv11(you only look once version 11) model, a two-stage end-to-end image-text fusion framework was created to increase recognition performance in complicated situations. In the initial stage, CBAM(convolutional block attention module), a color-space-based Mosaic-Focus method, and the Char-Focal-EIoU regression loss were combined into the backbone, while DT-NMS (dual-threshold non-maximum suppression) was employed in post-processing to boost small-target localization and improve the recall of low-confidence character instances. Paddle OCR was used in the second step to carry out structured character parsing, which made it possible to recognize important identifiers like UN numbers with greater accuracy. Experimental findings indicate that the proposed framework exceeds standard YOLO models in detection accuracy, character identification, and system responsiveness,demonstrate enhanced visual perception in challenging circumstances and confirm the better performance and usefulness of the suggested two-stage image-text fusion technique for identifying dangerous goods container signs.