To address the insufficient intelligent detection of surface damage on general aviation aircraft skins, an improved YOLOv11n-based detection algorithm is proposed. Firstly, the Adown downsampling mechanism is replaced to construct a multi-scale feature fusion architecture, achieving dynamic compression of redundant information through cross-level feature interaction and lightweight kernel design, thereby reducing model parameters and computational complexity. Secondly, a DySample dynamic upsampling strategy is designed, enhancing the model′s generalization across different scenarios via variable convolutional kernel deformation perception and multi-task gradient collaborative optimization. Furthermore, the FASSHead feature aggregation module is introduced, improving the algorithm′s recognition capability for complex damage areas through progressive semantic fusion and edge-aware constraints. Finally, a P2 small object detection layer is added, embedding high-resolution detection branches in shallow networks to enhance the capture of small objects and detailed damages. The improved algorithm was validated using a self-built dataset of general aviation skin surface damages. Results show that the precision reached 87.4%, recall reached 80.4%, and mAP attained 86.6%. Compared with the baseline model YOLOv11n, these metrics improved by 2.0%, 9.4% and 6.7% respectively, significantly enhancing the detection performance of skin surface damage and laying a theoretical foundation for an intelligent detection and maintenance system for general aviation aircraft.