Using Hybrid Algorithms of Human Detection Technique for Detecting Indoor Disaster Victims

Abstract

When an indoor disaster occurs, the disaster site can become very difficult to escape from due to the scenario or building. Most people evacuate when a disaster situation occurs, but there are also disaster victims who cannot evacuate and are isolated. Isolated disaster victims often cannot move quickly because they do not have all the necessary information about the disaster, and secondary damage can occur. Rescue workers must rescue disaster victims quickly, before secondary damage occurs, but it is not always easy to locate isolated victims within a disaster site. In addition, rescue operators can also suffer from secondary damage because they are exposed to disaster situations. We present a HHD technique that can detect isolated victims in indoor disasters relatively quickly, especially when covered by fire smoke, by merging one-stage detectors YOLO and RetinaNet. HHD is a technique with a high human detection rate compared to other techniques while using a 1-stage detector method that combines YOLO and RetinaNet. Therefore, the HHD of this paper can be beneficial in future indoor disaster situations.