FOD poses a great threat to airports. Detecting and removing FOD accurately and timely is the key point of airports’ safety work. A FOD detection algorithm that based on YOLOv7 is proposed to meet the requirements of accuracy and real-time. Firstly, the CBAM module is introduced into the backbone network to focus on the extraction of small target feature information from two aspects: spatial attention and channel attention. Secondly, the idea of AFPN is integrated into the feature extraction network and SA-PANet structure is proposed in combination with it. SA-PANet can asymptotically fuse adjacent effective feature layers and alleviate the semantic gap between them. Thirdly, the BiFormer module is introduced into the down-sampling branch of PANet, which can focus on further fusion extraction of small target feature information in the feature extraction network. Lastly, MPDIoU Loss is introduced into the boundary frame positioning loss calculation, which can not only accelerate the convergence of the model but also improve the detection accuracy and location accuracy. Experiments on FOD datasets show that the mAP50 of the improved YOLOv7 algorithm is 98.76%, which is 9.09 percentage points higher than the original YOLOv7. Comparing with other algorithms for FOD detection, the improved YOLOv7 algorithm has higher detection accuracy and the increase of Params and GELOPs is controlled within the acceptable range, which meet the accurate and fast requirements of FOD detection tasks.