To address the issues of feature resolution attenuation, size variation, and significant decline in recognition rate caused by dynamic changes in imaging distance during aircraft rivet damage detection, a viewing-distance adaptive detection method based on improved YOLOv11n was proposed. Firstly, leveraging the AirNet framework, a degraded representation space was learned by constructing positive-negative sample pairs, and a restoration network guided by degradation priors was employed to recover feature resolution in degraded images. Secondly, the dual-path spatial and channel cooperative reconstruction mechanism of SCConv was utilized, non-critical backgrounds in close-range rivet damage features were suppressed while simultaneously enhancing critical features in long-range rivet damage. Thirdly, a high-resolution 160×160 P2 detection layer was constructed to resolve the problem of rivet size variations resulting from fluctuating shooting distances. Finally, the DySample distance-aware convolution kernel deformation mapping function was introduced, enabling local sampling at close range to preserve details while enhancing the contextual semantics of long-range rivet damage features. This effectively improved the recognition rate for rivet damage. The effectiveness of the improved algorithm was validated using a self-built multi-viewing-distance aircraft rivet damage dataset. Results demonstrate that the improved algorithm achieved a precision of 92.1%, a recall of 83.2%, and an mAP@0.5 of 89.6%, representing improvements of 3.4%, 6.4%, and 5.3% respectively compared to the baseline YOLOv11n model. These results verify the effectiveness of the proposed viewing-distance adaptive detection method for aircraft rivet damage.