基于YOLOv5算法的无人机伤员搜救系统设计OA

The unmanned aerial vehicle(UAV)casualty search and rescue system is a technology that utilizes drones to search at disaster sites and locate injured individuals based on high-definition camera images.This method can efficiently search for injured or unconscious individuals,improve search and rescue efficiency,and reduce the risk of rescue personnel.This article introduces a unmanned aerial vehicle(UAV)casualty search and rescue system based on the YOLOv5 object detection algorithm.The system uses a convolutional neural network(CNN)model to achieve target detection of casualties,and uses the PyQt5 framework to design a graphical user interface that displays key images and text information on the screen,making it easy for search and rescue personnel to carry out their work.This paper introduces the hardware composition of four-rotor UAV,the principle of YOLO algorithm,the training of neural network model and the process of GUI software development,and simulates the photos of the wounded to carry out recognition experiments to verify the effectiveness and feasibility of the system,which provides a new idea and method for the development of UAV casualty search and rescue technology.