Abstract: Based on the response urgency requirements of terminals on emergency calls,this article optimizes the previous YOLOv5s object detection model to improve terminal situation emergency calls.There are three types of detection targets in the study:flames,smoke,and people's falls.Specifically,it improves the model by using MPDIoU as the loss function instead of original one,replacing NMS algorithm with softNMS algorithm and integrating the BiFormer attention mechanism into the small target detection layer.The study employes ablation experiments and comparative trials to validate the effectiveness of the improvements.Experimental results demonstrate that the enhanced model exhibits superior performance after training on a custom-built dataset,achieving significant improvements in the average precision metrics of mAP@0.5 and mAP@0.5:0.95,reaching 93.1% and 63.5% respectively.Compared to the original YOLOv5s model,these metrics increase by 1.7% and 4.2%,while outperforming the latest YOLOv11 model by 1.1% and 5% in the respective metrics.The better model works well with real-time video feeds from the airport terminals.It fulfills the requirements for incident detection and has high prospect of application at this kind of critical places.

Key words: Object detection, Terminal security, MPDIoU, softNMS, BiFormer