In order to solve the problems of high computational complexity and large computational resource consumption of the YOLOv8n algorithm in airport foreign object detection, the problem of airport foreign object detection was studied by introducing a lightweight module into the YOLOv8n algorithm, and the Fast-BiYOLOv8n algorithm was proposed. Firstly, the C2f_FasterEMA module was designed and introduced into the backbone network of the YOLOv8n algorithm. This module integrated the FasterBlock module and the EMA(efficient multi-scale attention) attention mechanism, which enhanced the feature extraction ability of the image and reduces the algorithm's computational load. Secondly, the P2 feature layer in the backbone network was integrated into the PANet(path aggregation network) network architecture, and the BiFPN(bidirectional feature pyramid network) network architecture was designed. The cross-scale connection was added to promote the information fusion between different feature maps. At the same time, the C2f_Faster module was added to improve the efficiency of feature fusion and further reduce the algorithm's computational load. Finally, the convergence speed of the algorithm was accelerated by improving the loss function to Inner-CIoU(intersection over union, complete intersection over union loss), and the detection accuracy was improved. The results show that the detection accuracy of the Fast-BiYOLOv8n algorithm reaches 99.0%, the recall rate is 98.8%, and the mAP (mean average precision) increases by 3.5 percentage points to 99.3%. The number of parameters is reduced by 27% compared with the original model, and the weight of the model is reduced by 21%, achieving the goal of improving detection accuracy while reducing the number of algorithm parameters.