Foreign object debris (FOD) on airport runways poses a significant threat to airline safety, making the study of FOD detection algorithms crucial for aviation security. Addressing the challenges of FOD detection, such as the small pixel occupancy of FODs in images, their lack of distinctive features, and their susceptibility to misdetection or omission, we propose an enhanced FOD detection method based on YOLOv5s, named VA-YOLO. The VA-YOLO method incorporates several key modifications to improve detection accuracy. First, a small target detection layer is added in the multi-scale fusion part to enhance the identification of small-sized targets, whereas the large target detection layer is removed to reduce computational load. Second, a general but efficient MobileViT block is introduced in the backbone section, replacing convolution with transformers to learn global representations. Third, the SKAttention module is integrated into the neck section to focus more on the target’s feature information. In addition, a hybrid loss function named normalized complete-IoU (NCIoU), is proposed, which combines the advantages of complete-IoU and normalized Gaussian Wasserstein distance to enhance detection accuracy. Comparative and ablation experiments were conducted on our self-constructed FOD-Z dataset. The experimental results, under the assumption of maintaining real-time performance, demonstrate that VA-YOLO achieves 99.2% mAP@0.5, 93.6% mAP@0.75, and 84.3% mAP@0.5:0.95. Compared with the original YOLOv5s method, the detection accuracy is improved by 1.4%, 3.5%, and 3.4%, respectively. Thus, VA-YOLO effectively addresses the issues of misdetection and omission of FODs on airport runways.