Foreign object debris （FOD） on an airport runway threaten aircraft safety during takeoff and landing， especially at night. This study introduces an intelligent vision algorithm to detect debris on airport runways at night. Considering the problems of existing models such as low detection accuracy owing to a tendency to focus on local features， a CSPTNet debris detection algorithm fused with self-attentional feature embedding is proposed. This algorithm replaces the standard BottleNeck module prevalent in conventional models with a Transformer BottleNeck module. In addition， the feature patch is flat segmented and embedded with position feature encoding to transform image representation from the pixel format to vector format. After capturing the relationship between the pixels in a high-dimensional vector space， the multi-head self-attention mechanism is employed to achieve the fusion of global and local features by obtaining feature information aggregated by different branches from the attention branch subspace. To solve the problems of blurred contour edges and difficult positioning due to the small scale of objects in datasets， we introduce the CIoU loss function to optimize predicted frame sizes and center positions. Thereby， the positioning accuracy of foreign object contours is enhanced. The experimental results show that the detection speed of this algorithm reaches 38 frames/s， which meets the requirements of real-time detection， and its average accuracy is 88.1%. Compared with the experimental results of the standard bottleneck module， the accuracy is increased by 5.7% through the Transformer BottleNeck module fusion with self-attentional feature embedding. In addition， compared with the state-of-the-art model YOLOv5， our is 5.2% more accurate. The obtained results demonstrate the effectiveness and engineering practicability of CSPTNet for FOD detection on airport runways at night.