One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology

The judgment of tunnel personnel’s safety status mainly requires the collection construction physical signs and cave environment data, early warning abnormal usually professional staff to make rapid judgments in a short time, which is costly inefficient terms operation maintenance. A single-classification support vector machine-based personnel detection model proposed address this phenomenon. First, by deploying sensor devices at site, we obtain data on state an actual scene construct OCSVM for prediction. Then retained testing, collecting relevant environmental as well from engineering examples. Finally, conduct horizontal different parameter experiments vertical proportional evaluate performance information security judgment. experimental results show that accuracy rate reaches more than 90%. In particular, it provides efficient means status.