Collapsing of structural buildings has been sighted commonly and the presence of potential faults has proved to be damaging to the buildings, resulting in accidents. It is essential to continuously monitor any building for faults where human access is restricted. With UAVs (Unmanned Aerial Vehicles) emerging in the field of computer vision, monitoring any building and detecting such faults is seen as a possibility. This paper puts forth a novel approach where an automated UAV traverses around the target building, detects any potential faults in the building, and localizes the faults. With the dimensions of the building provided, a path around the building is generated. The images captured by the onboard camera of the UAV are passed through a neural network system to confirm the presence of faults. Once a fault is detected, the UAV maneuvers itself to the corresponding position where the crack is detected. The simulation is done with ROS(Robot Operating System) using the AirSim environment which initializes ROS Wrappers and provides an integrated interface of ROS and AirSim to work with, The UAV is simulated in the same.
翻译:结构建筑物的碰撞是常见的,而且潜在故障的存在证明对建筑物有害,造成事故;必须不断监测限制人类出入的任何建筑物的故障;在计算机视觉领域出现了无人驾驶飞行器(无人驾驶航空飞行器),因此可以监测任何建筑物并发现这类故障;本文提出了一个新颖的办法,即自动无人驾驶飞行器在目标建筑物周围穿行,探测建筑物中的任何潜在故障,并将故障本地化;根据所提供的建筑物的尺寸,将产生建筑物周围的一条通道;无人驾驶飞行器机上摄取的图像通过神经网络系统传送,以确认是否存在故障;一旦发现故障,无人驾驶飞行器将自己操纵到发现裂缝的相应位置;利用AirSim环境进行模拟,启动ROS包装器,并提供ROS和AirSim的一体化接口;UAVAV是在同一环境中进行模拟的。