Abstract: Aviation luggage detection presents several challenges due to the wide variety of types, complex textures, and varying shapes of luggage, these challenges require deep learning models to have higher computational complexity to accurately identify the luggage targets. To improve the practical applicability and reduce model complexity, this paper proposes an aviation luggage detection method based on the lightweight YOLOv8-ASP. First, to optimize feature extraction and fusion while reducing model complexity and computation, an adaptive single PAN (path aggregation network) feature fusion network module is designed. Secondly, to enhance the perception of important features for aviation luggage, an efficient channel attention (ECA) mechanism is incorporated to the bottom of the YOLOv8n backbone network. Finally, to address the imbalance caused by scale variations of luggage targets, the MPDIoU bounding box loss function is introduced into the detection head. Experimental validation on an aviation baggage dataset demonstrates that, compared to the original YOLOv8n model, the proposed YOLOv8-ASP method achieves a 0.4% improvement in mAP@50, a 16% improvement in FPS, while reducing the parameter count from 3.012×10⁶ to 2.759×10⁶ and GFLOPs from 8.2 to 5.6. The experimental results show that the proposed method significantly reduces model complexity and computational cost while maintaining detection accuracy.