Object detection and hazard avoidance with FPV equipped single-rotor UAVs

无人机(UAV)**系统的障碍物检测和危险规避存在设计挑战。我们的研究通过分析工具、技术和架构来应对这一挑战，这些工具、技术和架构帮助**开发人员构建配备了高效障碍物检测和危险**避免方法的健壮和可靠的无人机。诺瓦里亚机器人公司是一家无人机制造商，其中一款广受欢迎的无人机是Procyon 800e**，它是一种单旋翼直升机式无人机。诺瓦里亚机器人公司操作无人机的挑战之一是**检测不同环境条件下的物体。因此，在这个项目中，我们的目标是研究**并提出解决方案，以解决配备第一人称视角**(FPV)的单旋翼无人机**的目标检测和危险规避问题。**无人机需要在各种动态情况下提供适当适应的可靠服务。系统**需要跟踪所有情况才能正常运行。系统有必要通过监控其环境来充分**了解其不同的条件和属性。成像技术和通信的最新进展带来了令人难以置信的增长，各种不同的相机和**各种视觉设备作为一种强大的监控工具。此外，视频提供了有关**交互的详细信息，而不是跟踪或数据记录。通过对视频数据的分析，我们可以检测到不同的事件**，并识别它们之间的交互，包括时间属性。该项目旨在分析从操作无人机收集的视频流**以进行障碍物检测和危险避免。

Design challenges exist for obstacle detection and hazard avoidance of unmanned aerial vehicle (UAV)**systems. Our research addresses this challenge by analyzing tools, techniques and architectures that assist**developers in building robust and reliable UAVs equipped with efficient obstacle detection and hazard**avoidance methods. NOVAerial Robotics is a manufacturer of UAV and a well-received one is Procyon 800E**which a single rotor helicopter style UAV. One of the challenges in operating UAVs by NOVAerial robotics is**the detection of objects in different environmental conditions. Therefore, in this project we aim to investigate**and propose solutions to address the problem of object detection and hazard avoidance in first person view**(FPV) equipped single rotor UAVs.**UAVs need to provide reliable service with appropriate adaptation in various dynamic situations. The system**needs to keep track of all the situations for proper functioning. It is necessary for the system to have sufficient**knowledge of its different conditions and properties by monitoring its environment. Recent advances in**imaging techniques and communications have introduced an incredible growth of different cameras and**various visual devices as a powerful monitoring tool. Moreover, video provides detailed information on the**interactions as opposed to tracing or data logging. Through analyzing video data, we can detect different events**and identify their interactions including timing properties. This project aims to analyze video streams gathered**from operating UAVs for obstacle detection and hazard avoidance.