A NEW METHOD FOR DISASTER VICTIM DETECTION USING YOLO ALGORITHM INDEX

Abstract

ABSTRACT: Post-disaster environments resulting from catastrophic events, leave sequels such as victims trapped in debris, which are difficult to detect by rescuers in a first inspection. It is very important to maximize the chances of detecting trapped persons in collapsed buildings. Our designed system with the meteoric embedded systems along with microcontroller is preventing deaths and providing safely guided measures. We focus on the detection of such victims using deep learning, and we find that state-of-the-art detection models pre-trained on the well-known COCO dataset fail to detect victims. This is because all the people in the training set are shown in photos of daily life or sports activities, while people in the debris after a disaster usually only have parts of their bodies exposed. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. Many of those recent research articles discuss automated machine learning approaches to extract disaster indicating posts, useful for coordination from various social media posts.. This research proposes the development of a method for the detection of victims of natural disasters that aims to assist the SAR team and natural disaster volunteers in searching for victims who are in hard to reach places. The You Only Look Once (YOLO) method is implemented. We use a piezoelectric plate to sense the vibration created by trapped person in disaster area so the microcontroller connected is to collect the data and sent the data to the data collecting unit (DCU) by using Wireless transmission. We propose a framework to generate harmonious composite images for training. We first paste body parts onto a debris background to generate composite victim images and then use a deep harmonization network to make the composite images look more harmonious. We select YOLOv5l as the most suitable model