To address the problems of easy missing, poor robustness and generalization ability when object detection model is detecting a person falling down, a new detection method YOLOv5s-FPD was proposed based on the improved YOLOv5s. Firstly, the Le2i fall detection data set was expanded in various ways for model training to enhance model robustness and generalization ability. Secondly, MobileNetV3 was employed as the backbone network for feature extraction, which could coordinate and balance the relationship between lightness and accuracy of the model. Furthermore, BiFPN (bi-directional feature pyramid network) was utilized to boost model multi-scale feature fusion ability, thereby improving the efficiency and speed of fusion. Meanwhile, the CBAM (convolutional block attention module) lightweight attention mechanism was adopted to realize double focus attention to channel and space, enhancing the effect of attention mechanism on model accuracy. Finally, Focal Loss evaluation was used to pay more attention to hard example mining and alleviate the samples imbalance problem. The experimental results show that the precision, F1 score, and detection speed of YOLOv5s-FPD model were improved by 2.91%, 0.03, and 8.7 FPS, respectively, compared with the original YOLOv5s model on Le2i fall detection dataset, which verified the effectiveness of the proposed method.