In the fields of low-altitude economy and civil aviation, security assurance tasks heavily rely on the efficient correlation of cross-modal information such as images and texts. However, while mainstream cross-modal retrieval models perform well on general datasets, they underperform in these areas, which require high levels of fine-grained semantic understanding. Based on existing civil aviation datasets, a cross-modal retrieval approach with fine-grained semantic augmentation is suggested as a solution to this problem, creating a whole pipeline that includes data processing, model development, and training. First, text descriptions are optimized and enhanced based on a large multimodal model to construct a cross-modal retrieval dataset containing rich semantic information. Second, a model is created using a popular cross-modal retrieval framework. To improve the model's ability to express fine-grained semantic features, techniques such class supervision, key semantic information masking, and a fine-grained feature extraction module are introduced. Experimental results on two datasets verify the effectiveness of the proposed method, providing a reference technical path for cross-modal retrieval in low-altitude economy scenarios.