To address the impact of surface defects in aircraft skin—critical structural components—on overall structural performance, this paper proposes a deep learning detection network based on the RT-DETR model, aiming to enhance the accuracy and robustness of aircraft skin defect detection. In response to the challenges posed by defects of varying scales, morphologies, and complex distributions, a series of innovative techniques were adopted for optimization. First, in the feature extraction stage, multilevel convolution blocks (MCB) were introduced. Through multi-layer convolution operations, the discriminative power of features at different scales was strengthened, effectively capturing details at various levels. Second, in the feature fusion stage, a multiscale feature enhancement (MSFE) module was employed, using multi-size depthwise convolution kernels to build contextual information, thereby improving the network’s robustness and adaptability to multiscale defect features. Finally, in the regression stage, a shape-aware (Shape-IoU) optimization module was introduced, which optimizes the matching between bounding boxes and defect contours, significantly improving detection accuracy. Experimental results show that the proposed network achieved an mAP@0.5 of 94.8% on the Aircraft dataset, representing a 12.7% improvement over the original RT-DETR model. Furthermore, on the NEU-DET test set, the model attained an mAP@0.5 of 92.5%. These results validate the effectiveness of the model in improving both the precision and generalization capability of aircraft skin defect detection.